Datasets:

CompEpigen
/

syto.1.0

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+MANIFEST_CONTENTS.csv filter=lfs diff=lfs merge=lfs -text
+MANIFEST_INTERNAL.csv filter=lfs diff=lfs merge=lfs -text

FILE_NAMING_CONVENTION.txt ADDED Viewed

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+SYTO PUBLICATION DATA - FILE NAMING CONVENTION
+==============================================
+Last updated: 2026-08-24
+This file documents the naming convention used in this dataset, together with
+every abbreviation and code that appears in a file or folder name.
+1. GENERAL RULES
+----------------
+- Words within a name element are joined without separators (e.g. "softlabels");
+  underscores (_) separate the elements themselves.
+- Directory names are lowercase with hyphens between words (tier0-results,
+  rrbs-recovered-reads). Result file names use the element scheme in section 3.
+- Names are location-independent: a file still identifies itself after being
+  moved out of its folder.
+- Version is a trailing _v<N>. v1 is the first published release.
+2. TOP-LEVEL LAYOUT
+-------------------
+The deposit ships as archives, one per logical unit, plus a few loose files
+that should be readable without downloading anything large:
+  tier0-results/                	Arhives with final result for two main experiments
+  					+ TabulaSapines mappings + consolidated ranking table
+  					of Syto variants and external baselines.
+  tier1-models/<experiment>/    	Trained Syto variants, one archive per variant.
+  tier2-pseudobulks/<experiment>/	One archive per pseudobulk run.
+  tier3-training-data/          	Marker atlases used in Syto paper; target proportions
+  					used for each pseudobulk generation;
+  					training datasets under datasets/<genome>/.
+  tier4-source-data/            	Staged and recovered reads, plus the hg19 and
+                                	hg38 reference genomes.
+  MANIFEST.csv                  	One row per archive or standalone file: what it is, size, file
+                                	count, sha256.
+  runs.csv                      	One row per experiment run.
+  FILE_NAMING_CONVENTION.txt    	This file.
+Unpacking every archive restores the original tree:
+  tier0-results/          Final tables underlying the publication.
+  tier1-models/           Trained classifiers, deconvolvers and calibrators.
+  tier2-pseudobulks/      Generated pseudobulk mixtures (columnar Parquet).
+  tier3-training-data/    Atlases, target proportions and training datasets.
+  tier4-source-data/      Staged and recovered reads, and reference genomes.
+Within tier1 and tier2, folders are <experiment>/<run_name>, where run_name is
+<classifier>__<labelmode>, e.g. dismir__softlabelpooled.
+3. RESULT FILE NAME SCHEME (tier0-results)
+------------------------------------------
+Six elements:
+  <project>_<experiment>_<classifier>_<labelmode>_<featureset>_<version>.csv
+  Example:
+    SYTO_oodrrbs_dismir_softlabels_top156_v1.csv
+     |    |       |      |          |      |
+     |    |       |      |          |      +-- version
+     |    |       |      |          +--------- feature set
+     |    |       |      +-------------------- label mode
+     |    |       +--------------------------- classifier
+     |    +----------------------------------- experiment
+     +---------------------------------------- project
+4. CODES AND ABBREVIATIONS
+--------------------------
+PROJECT
+  SYTO              The framework introduced in "data-driven soft labeling scales
+  		    dna read classification to whole-body cell-type deconvolution".
+EXPERIMENT
+  MAIN EXPERIMENTS:
+  pseudobulk           [pseudobulk] In-distribution evaluation on generated
+                       pseudobulks.
+  oodrrbs              [ood-rrbs] Out-of-distribution evaluation on the cfSort
+                       RRBS cohort.
+  SUPPLEMENTARY EXPERIMENTS:
+  oodrrbsgss           [ood-rrbs-gss] Out-of-distribution RRBS using the
+                       GSS-sorted atlas.
+  poolingsensitivity   [pooling-sensitivity] Sensitivity analysis over
+                       read-pooling thresholds.
+  mledeconvolvers      [mle-deconvolvers] Maximum-likelihood deconvolver
+                       variants.
+CLASSIFIER
+  dismir            Syto-extended DISMIR read-level classifier (CNN-LSTM).
+  methylbert        Syto-refactored MethylBERT transformer classifier.
+  lookup            Syto-introduced 1NN-Lookup-table classifier.
+  cancerdetector    Syto-extended CancerDetector baseline classifier.
+  baselines         Not a classifier: reference deconvolution methods
+                    (Celfie, EpiDISH, Houseman CP, UXM) collected in one table.
+LABELMODE  (how read-level training labels were assigned)
+  hardlabels        Hard labels with a background class.
+  softlabels        Data Driven Soft labels, with read pooling.
+  softnopooling     Data Driven Soft labels, without read pooling.
+  softcanonical     Soft labels produced by label smoothing (a.k.a canonical).
+  uniformprior      CancerDetector with a uniform cell-type prior.
+  trainfreqprior    CancerDetector with a training-frequency prior.
+FEATURESET  (which probability-simplex scores were selected for deconvolution)
+  top156            The top 156 features (see procedure in the paper).
+  diagbackground    Diagonal-background feature selection (see procedure in the paper).
+  gssatlas          These runs also use the top-156 feature set but the entire pipeline is
+                    run on Proxy-GSS-sorted atlas.
+VERSION
+  v1                First published release.
+REFERENCE GENOME (appears in tier3/tier4 folder names)
+  hg19, hg38        Human reference genome builds.
+                    Taken from wgbs_tools;
+ATLAS NAMES (tier3-training-data/atlases)
+  U25, U250         Loyfer atlas variants (25 or 250 regions per cell type).
+  l4                The minimum length of called CpGs in read/fragment
+                    considered when computing atlas values.
+  GSS_SORTED        Proxy Gap Specificity Score (GSS) sorted atlas.
+                    Each cell type is represented by the top 25 regions
+                    starting from the highest GSS
+  trainonly         Atlas built from the training split only. Otherwise,
+                    atlas built from the entire dataset
+5. ARCHIVE NAMES
+----------------
+Archives follow the same element style as result files: project first,
+version last, underscores between elements, no spaces.
+  <project>_<tier>_<...unit...>_<version>.zip
+The middle elements identify the unit and vary by tier:
+  tier0  one archive per result group
+         SYTO_tier0_results_baselines_v1.zip
+         SYTO_tier0_results_cfsortooddeconvolutionresults_v1.zip
+  tier1  one archive per trained model (experiment + classifier + label mode)
+         SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip
+  tier2  one archive per pseudobulk run, same elements as tier1 (pseudobulk is
+         build on the classifiers outputs and the mapping is one-to-one)
+         SYTO_tier2_pseudobulks_pseudobulk_methylbert_softlabelpooled_v1.zip
+  tier3  one archive for atlases, one for target proportions, one per dataset
+         SYTO_tier3_trainingdata_atlases_v1.zip
+         SYTO_tier3_trainingdata_hg38_oodalldatalabels_v1.zip
+  tier4  one archive per source dataset, and one per reference genome
+         SYTO_tier4_sourcedata_stagedu250_hg38_v1.zip
+         SYTO_tier4_sourcedata_referencegenomes_hg38_v1.zip
+USING THE ARCHIVES
+------------------
+Members are stored with their path relative to the DEPOSIT ROOT.
+Always unpack from the root, whichever folder an archive came from:
+    cd /path/to/syto-publishable-data
+    find . -name 'SYTO_*.zip' -exec unzip -o -q {} ';'      # everything, or
+    unzip tier1-models/ood-rrbs/SYTO_tier1models_oodrrbs_dismir_softlabelpooled_v1.zip
+Unpacking an archive from inside its own folder would nest the tree a second
+time (tier1-models/ood-rrbs/tier1-models/...) and the configs would not
+resolve.
+The reference-genome archives contain relative symlinks (genome.fa.gz ->
+hg38.fa.gz) matching the wgbs_tools layout.  Unpack them with a tool that
+preserves symlinks; unzip on Linux and macOS does.
+6. NOTES ON CONTENT NOT ENCODED IN NAMES
+----------------------------------------
+- Missing-label strategy: every cfSort deconvolution result in tier0 was
+  produced with the prior blending strategy (see the paper).
+- Deconvolver and calibrator are NOT in the file name. They vary within a
+  file and appear as the "Deconvolver" and "Calibrator" columns instead.
+- All experiments were originally recorded using our local mlflow registry.
+  For the purposes of sharing we have stripped the data from the mlflow
+  related files, however, each element in tier1-models contains recorded
+  metrics, parameters and tags from classifier fitting. Hence, it should
+  be possible to create your own syntetic meta.yaml files to access
+  these metadata via mlflow.
+7. CONFIG PATHS
+---------------
+The published YAML configs use paths relative to that same root, so they
+resolve once the archives holding the files they reference are unpacked. A
+config will not find its inputs while they are still inside a zip.
+The configs were automatically redacted for this data packaging purposes,
+meaning that original configs were referring to different paths in either
+logical filesystem or HPC. We didn't re-run all experiments after re-packaging
+configs. Therefore, shared configs must be treated as the reference points,
+meaning that the parameters are as in original experiments. Still, users
+may need to correct pointers to the paths depending on how they unpack
+the data.
+Two placeholders mark inputs this release does not redistribute:
+  ${LOYFER_RECOVERED_READS}   Loyfer recovered read tables: outputs of
+                              https://github.com/CompEpigen/wgbs_atlas_simulation
+  ${SYTO_MLFLOW}              An MLflow store, for the intermediate
+                              *_predicted.pkl inputs that are not published.
+                              Can also be any other intermediate store.
+The *_predicted.pkl were the original data splits enriched with the outputs
+of the trained classifiers that were used as inputs to pseudobulks generation.
+The pseudobulk pipeline is available to run in predictions_only mode, which
+allows producing such files. This is handy when, for example, we need
+to use a GPU-heavy classifier (like MethylBERT) for predictions, and we want
+to run this stage on a more expensive GPU node, deferring pseudobulk generation
+to a CPU-only node. While we did not publish those files, we have included two-stage
+configs where relevant so users can reproduce pseudobulks.

MANIFEST.csv ADDED Viewed

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+item,tier,description,bytes,files,sha256
+tier0-results/SYTO_tier0_results_baselines_v1.zip,tier0,Final result tables: baselines,381705958,37,1d6f5bc7bebeda1af11016f3c579627afde81b7efdcc6c61bcd094d963f6aedb
+tier0-results/SYTO_tier0_results_cfsortooddeconvolutionresults_v1.zip,tier0,Final result tables: cfsort_ood_deconvolution_results,72581474,22,8c260e3e79419b20ce4e64a9ea434145f34e8a38f4e31f67417f9d0eae3aa9e0
+tier0-results/SYTO_tier0_results_mappings_v1.zip,tier0,Final result tables: mappings,10872,4,89f31478a4fcdd5167c66cf4b93a3c0d6dda232718675e71cff012a9ca1b690b
+tier1-models/mle-deconvolvers/SYTO_tier1_models_mledeconvolvers_methylbert_hardlabelwithbackground2labels_v1.zip,tier1,Trained model: mle-deconvolvers / methylbert__hard_label_with_background__2labels,665930255,265,c2476d9529842370e0676881e557d377531aa8d0ad3950ca28a77f1b55d51b28
+tier1-models/mle-deconvolvers/SYTO_tier1_models_mledeconvolvers_methylbert_hardlabelwithbackground40labels_v1.zip,tier1,Trained model: mle-deconvolvers / methylbert__hard_label_with_background__40labels,665865023,265,ef631e30b67db8a621098c961840d6e67fa5c49286a1d2e0bca61ad02b9fb8c8
+tier1-models/ood-rrbs-gss/SYTO_tier1_models_oodrrbsgss_dismir_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs-gss / dismir__hard_label_with_background,363485581,1091,d332f33056de1b33932e507f3ea7332e12d15609c484327a6b2312fca6c47268
+tier1-models/ood-rrbs-gss/SYTO_tier1_models_oodrrbsgss_dismir_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs-gss / dismir__soft_label_no_pooling,353070174,1091,5eec484facaad6b0445a1572aaab5e5681957f6ba402d9c245f7c77439324ccd
+tier1-models/ood-rrbs-gss/SYTO_tier1_models_oodrrbsgss_dismir_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs-gss / dismir__soft_label_pooled,353393718,1091,799dbde8903b2593be346062e44e16c451046b5e3623c6159fea749231f14a73
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_cancerdetector_trainfreq_v1.zip,tier1,Trained model: ood-rrbs / cancer_detector__train_freq,362169897,1080,2898a4f59932eff7a38cc9a5e8fbbb05367206a5688ece2125909d827f841931
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_cancerdetector_uniform_v1.zip,tier1,Trained model: ood-rrbs / cancer_detector__uniform,360648042,1080,579c2e5afc7d5b9ebe4369598950543f7827dfe72f92b9b83d09596e6d775715
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: ood-rrbs / dismir__hard_labels_background_and_smoothing,602669243,2156,ca24a5611e132669f4907226234f316f5a8b2f13296154fcee8190ef96909e4e
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs / dismir__hard_label_with_background,611597224,2156,7bc312decf10861644e59a405e433395bf439c9ee3ce2f4a58d59fae38fd49ca
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs / dismir__soft_label_no_pooling,352901063,1091,08a9934de4bd852a5d6d8b1208c7abfb04d90bbae2d6a0edaf3d5fe853bebd2a
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs / dismir__soft_label_pooled,359334740,2133,c2c39463df96fa565f9e723962383e2ef391b8f90885283d000140b75c579cad
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_lookup_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs / lookup__hard_label_with_background,612796679,2148,f370e50d1027dc3b52fb9f4e2c92b4e1cabd7c77964faff9fb5b84059616afa6
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_lookup_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs / lookup__soft_label_no_pooling,361312445,1083,4ca2dcdcfa6c53faddf2c8b04ef0707c1f328e704454ec03cf4f3eb090bcf1fa
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_lookup_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs / lookup__soft_label_pooled,453917777,1083,cf04adadfc7ca74feaa3306ac59297f744721fbb401e187f0097b9ea212b9d44
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: ood-rrbs / methylbert__hard_labels_background_and_smoothing,1303272595,2391,34d2b230057c7b4fb2f0e6e624cc7e7707103d81fe3a9871209eefd7cd7fc9e2
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs / methylbert__hard_label_with_background,1319976544,2391,cdfdfe13bd633068bd6634361e14bda10e25275c97260d579cf1af5257053f0b
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs / methylbert__soft_label_no_pooling,1028690663,1326,a30ecd130261919dcaec69b919aa1a61e269f3b7364ebf9b4f430bb0fee049d3
+tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs / methylbert__soft_label_pooled,1026257958,1326,0929195bef7f4e725a8b13e608df06fb0d9b86ab599e6cf40de396f4be18df29
+tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledminreads15_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_minreads_15,470839936,51,8dbfdfecab11d9cefca43b6c98b5a8b7c265079b43a6c3023c9051f0bd0d74fa
+tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledminreads45_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_minreads_45,471222034,51,fac3d682b16b7c68a85d7b60dce13d3c7041ea6934a74347b2daee20c79d4aa6
+tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledtr02_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_tr0-2,467369250,51,743a317b77076a4acfc8894b58f171f6bca33169a4202c5bcf60a2afbc283c2f
+tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledtr06_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_tr0-6,472111813,51,2f9a69cb4a4d31fd87423bad55282810b37e5af9a7f43ace339a520653e13b5e
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_cancerdetector_trainfreq_v1.zip,tier1,Trained model: pseudobulk / cancer_detector__train_freq,467725310,40,c7c846390d55532a84c82be16b2a5053fabfe8f8e3ef29c3c198678842fb1f46
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_cancerdetector_uniform_v1.zip,tier1,Trained model: pseudobulk / cancer_detector__uniform,466179865,40,677de88ac2dc1b9f8af79aea22fa20ee3f3ac091c398f0f0e15789ab05126423
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: pseudobulk / dismir__hard_labels_background_and_smoothing,772247453,75,be3dca5b671599fb4944e07d672169b21242d7962fc9b6d2e091d613611a871e
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_hardlabelwithbackground_v1.zip,tier1,Trained model: pseudobulk / dismir__hard_label_with_background,789477336,75,c7444e5a57350d03ed5740875bbe598afde2744f0057b4d63ec183d6e2c02318
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_softlabelnopooling_v1.zip,tier1,Trained model: pseudobulk / dismir__soft_label_no_pooling,460267665,50,c6ad4824a18394838d5992c5a00ff7adfb7acdd178d5411e3f56028adf6883cb
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_softlabelpooled_v1.zip,tier1,Trained model: pseudobulk / dismir__soft_label_pooled,941379246,62,c9f2c402e85cc323e0b9caad21bf6df4624aac8c02d4dc5e23fb0f9495f756b7
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_lookup_hardlabelwithbackground_v1.zip,tier1,Trained model: pseudobulk / lookup__hard_label_with_background,728050127,67,83c9ab9432ed965cb77e29f87ade28ccd00b2752432d86f83ab6131e0172a979
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_lookup_softlabelnopooling_v1.zip,tier1,Trained model: pseudobulk / lookup__soft_label_no_pooling,474549752,42,934a86bbfd97fce7631157bf24940a5d7466ab1d4e963bf136c6d6bccc26cdbf
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_lookup_softlabelpooled_v1.zip,tier1,Trained model: pseudobulk / lookup__soft_label_pooled,553777225,42,460006a4159befaab1302a0618031f0fcd4c288b5d078ce92d2c3a5b26d3ec32
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: pseudobulk / methylbert__hard_labels_background_and_smoothing,1489088874,314,4e2ed4cd2739dca38ee16440f42d8690eb20c909e77df9fc938069ae42186ddb
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_hardlabelwithbackground_v1.zip,tier1,Trained model: pseudobulk / methylbert__hard_label_with_background,1499403107,311,721ab88e76b0c27ade7b9b88b6789b42510328f38e80df7422b95f3fcb3b77bd
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_softlabelnopooling_v1.zip,tier1,Trained model: pseudobulk / methylbert__soft_label_no_pooling,1147747261,289,47b75a1988243340591c63a694362bd18b718b97b8b9d5730babb338bd913cf5
+tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_softlabelpooled_v1.zip,tier1,Trained model: pseudobulk / methylbert__soft_label_pooled,1123597897,286,a6e042db8fd931f7776433347fe12078c848f4dd6c9308ea96fc6620c3934b7d
+tier2-pseudobulks/ood-rrbs-gss/SYTO_tier2_pseudobulks_oodrrbsgss_dismir_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs-gss / dismir__hard_label_with_background,3944107181,15,46ad82ac6dccc5c88ff655cc8545f5fd9bbcd20a67c0344787502857b9f1397d
+tier2-pseudobulks/ood-rrbs-gss/SYTO_tier2_pseudobulks_oodrrbsgss_dismir_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs-gss / dismir__soft_label_no_pooling,3570055512,15,2aac9e78ccc287c27c38320ec39c661dba1bc09fead268ef5296cef1251b319a
+tier2-pseudobulks/ood-rrbs-gss/SYTO_tier2_pseudobulks_oodrrbsgss_dismir_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs-gss / dismir__soft_label_pooled,3519805299,15,1a36b3c29b1d054c35b222438f0275feddc9b8a343443b9092a96c4374563916
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_cancerdetector_trainfreq_v1.zip,tier2,Generated pseudobulks: ood-rrbs / cancer_detector__train_freq,3374107603,14,59825b139e257b9a1aadf2da4ee5d7c63060d64166e3219f73637eaf14fd520b
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_cancerdetector_uniform_v1.zip,tier2,Generated pseudobulks: ood-rrbs / cancer_detector__uniform,3380117438,14,6dfe875a9d157102b03967766bdf96a02bf9d2b0bdcf25cc30b0ad4b321faed6
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__hard_labels_background_and_smoothing,4045149674,15,825a521d714d42ff4a33a37c4b5a7e90903311c26e99729fba93b6a99eac7733
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__hard_label_with_background,4430893921,15,eef85da56aa79ae5f699e21faeeae3a301c439a5e08f506c0cdc5973a35393e9
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__soft_label_no_pooling,4230845535,15,0c895c20a91255338c9236db003d3b0c0e1ea65f1fc5f7d63e011e74e159b22e
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__soft_label_pooled,3815064441,15,15f57c558acd3244e2f820595e66decb078b8bc598ab93208f5ecc6d21e4707e
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_lookup_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs / lookup__hard_label_with_background,417511194,14,7fc3b0f77796004dbfca8ca79f3073ea0845728bb0570598a80d3be9e8a7253e
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_lookup_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs / lookup__soft_label_no_pooling,3355329242,14,ba6e99e93a7c2ef1d73d38f0cc79989eb5380b9db1a92c377aca926da243eccf
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_lookup_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs / lookup__soft_label_pooled,3459660850,14,1f53ee0064bcc7cd8a6fc7c7c7065b78a2b7b8fad63ffc023fcffcfb1f900baf
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__hard_labels_background_and_smoothing,3889871590,15,026817de49270afbc92af731313a6afe731f315cfebfafb8a7b120ea70a65f51
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__hard_label_with_background,3983578715,15,9562e66955ca0b5560557905ea22e3e1e562e141b3fec62ec525f319377e8944
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__soft_label_no_pooling,3805019954,15,89da0525082b73d568814139a60b8be609f7c0093b4e286886926cb4dbb54f57
+tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__soft_label_pooled,3770840907,15,6710154f5361cad6658e7af80aefaf6d617c79861ded2efc32fcbbf133a4b3b0
+tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledminreads15_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_minreads_15,5244756628,20,80400bb8540b3784fe95c9bc131fd3cb4d99df1734cdfa49b572ac415456914a
+tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledminreads45_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_minreads_45,5157046700,20,763e4b943295012aad0c51437a1072440257ac17dc94a68bb5978332e49e7a34
+tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledtr02_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_tr0-2,5166704118,20,3ed688be92090a3323f590cdac21744e2d10b31c52a935ce3075373d73dfd5e5
+tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledtr06_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_tr0-6,5118756155,20,f9eb8858497cf21ebc50dbea41183e9454c9982c81dd9ba6c267323396a0f26f
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_cancerdetector_trainfreq_v1.zip,tier2,Generated pseudobulks: pseudobulk / cancer_detector__train_freq,4923202430,19,44169c7554cf7e2945a77b775db48ad7f6f6dc5575871a756e6adb1bf4ef8138
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_cancerdetector_uniform_v1.zip,tier2,Generated pseudobulks: pseudobulk / cancer_detector__uniform,4932406335,19,2521b7dc7b16569713c576d36edffbbc5c1b8d5241754a6178e553c28bfc75f0
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__hard_labels_background_and_smoothing,5345266035,20,da496fd0886cbc88727527b7b2ef7a337246db919e3bcee176dbaa11a3ad88fe
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__hard_label_with_background,5622343855,20,537063c9fa607454a3148922df25c3a56c6335678947266319f3f56d1c37519c
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__soft_label_no_pooling,5383182614,20,71f2458cfc971744da4312345d82edd3470053ca04f4d06e3488dbc908a9f3cd
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_softlabelpooled_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__soft_label_pooled,5077217404,19,37b788f2fc1f83fb201ed3006cf34c3ce7004f0672e23301fd07353491152751
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_lookup_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: pseudobulk / lookup__hard_label_with_background,611084614,19,b9aafdc62bc64cc6d6841221029605fec168f4d0030cdb85012338afef735660
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_lookup_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: pseudobulk / lookup__soft_label_no_pooling,4959426613,19,fd568db7b25e3ce88cbd1dd941504425c9c4e0c633fe77093bf984857c1f1991
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_lookup_softlabelpooled_v1.zip,tier2,Generated pseudobulks: pseudobulk / lookup__soft_label_pooled,5065703160,19,f5fa53762ab1badfd2b4d879becd452296f5c57f06cc749a9f1e84f6ffa0b57c
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__hard_labels_background_and_smoothing,5566090386,20,fe5e590c55c65caacf6d75cc490669875071cd59263596b585373c99da8e2de3
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__hard_label_with_background,5655587087,20,e29d13082ed0e58bded6ea8a6783af40dd793feb4438fe91962fad916d7ffd3c
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__soft_label_no_pooling,5414865692,20,b47504f88281c26ba9fb01c104c36d1c9061f68b3bc996bda6033cb33b51d0a7
+tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_softlabelpooled_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__soft_label_pooled,5306983642,20,eec47aa85d1e59cede089d9594bc399c1ff80421f95f741ef4a41d9c64abc821
+tier3-training-data/SYTO_tier3_trainingdata_atlases_v1.zip,tier3,Training inputs: atlases,4897139,10,e1e132fd87ba58f341f9154a27a077a3dca15a0665664fc7da60f65f7ad41459
+tier3-training-data/SYTO_tier3_trainingdata_targetproportions_v1.zip,tier3,Training inputs: target proportions,16675054,3,6455a3e2316a0f99a512ac20223ade5cc71782381e89dfb197e79bc491911cc1
+tier3-training-data/datasets/hg19/SYTO_tier3_trainingdata_hg19_oodalldatalabels_v1.zip,tier3,Training dataset: hg19 / ood-alldata-labels,2174012872,129,a457ea5c8dd6b2b538943dfdec85184a153a466f8ef852284e8b357cfc51f3e5
+tier3-training-data/datasets/hg19/SYTO_tier3_trainingdata_hg19_pseudobulktrainlabels_v1.zip,tier3,Training dataset: hg19 / pseudobulk-train-labels,1955902581,129,3de3b5898a843a10ba032a65484fd8b956690f9869c9d18ffcfaa958af360c46
+tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_oodalldatalabels_v1.zip,tier3,Training dataset: hg38 / ood-alldata-labels,2373573227,129,7b26c7648e5714aa70987affe238b525ab21f07b23728c030eb103745e2e0a2b
+tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_oodgssalldatalabels_v1.zip,tier3,Training dataset: hg38 / ood-gss-alldata-labels,2131405884,129,312c72ee5c68ac4ab5a3a40a2b484b2d3b9e6a5e28c066e647b2a6660d0cb0e5
+tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_poolingsensitivitylabels_v1.zip,tier3,Training dataset: hg38 / pooling-sensitivity-labels,1825093363,129,c9b5e37197a2b502b588b5a4d8abdbdc084418699723014643e0a6db7d8264c9
+tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_pseudobulktrainlabels_v1.zip,tier3,Training dataset: hg38 / pseudobulk-train-labels,2124803297,129,5795a02eea819ee0391900a4b120ba46b7028c8827899958d8ba9d057a9e5038
+tier4-source-data/reference-genomes/SYTO_tier4_sourcedata_referencegenomes_hg19_v1.zip,tier4,Source data: reference-genomes / hg19,1115262363,15,4cf364ddf4542de4478aab0e74f9c3f380f1e5308328f9d3cb9f82cc8df71569
+tier4-source-data/reference-genomes/SYTO_tier4_sourcedata_referencegenomes_hg38_v1.zip,tier4,Source data: reference-genomes / hg38,1153763877,13,828f1f72800c97bf49a858cea9ce7946a5cf295a73484fd58a550f9a31f8e977
+tier4-source-data/rrbs-recovered-reads/SYTO_tier4_sourcedata_rrbsrecoveredreads_U250l4hg19_v1.zip,tier4,Source data: rrbs-recovered-reads / U250.l4.hg19,1184122671,521,f402f60fd129bd3d4db9861a8b41e33400912d1ce98e537508c050f8dafe58b4
+tier4-source-data/staged-u250/SYTO_tier4_sourcedata_stagedu250_hg19_v1.zip,tier4,Source data: staged-u250 / hg19,4099519881,26449,ac5960d8eaecbe946811ac19a43268cb43b2236c83a6c8b22cb9088c9e46df35
+tier4-source-data/staged-u250/SYTO_tier4_sourcedata_stagedu250_hg38_v1.zip,tier4,Source data: staged-u250 / hg38,4235769475,26449,1b8fbfee12e2c910a5b8406890442cd45dcecea12ff4f0c098e2adf9dec245b3
+FILE_NAMING_CONVENTION.txt,,"Naming convention, abbreviations and how to unpack the archives",10408,1,d96f337e133a1858b6b98b57ca899d38388bc100d8f1825111929877359362cf
+runs.csv,,One row per experiment run,13911,1,b8a7f92c74fcb8b358d384febfde8f49a362d9b5a06958c47e060adc2fb8b3a9
+tier0-results/SYTO_tier0_results_jointexperimentsranking.tsv,,Loose file,88806,1,48c8dbd3e4413d9a0975bad3671dce537df77417791143654356649a59e95e62

MANIFEST_CONTENTS.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:4f23c7854200e9a436e7f4187e324fe13c90032e335c01ea592a9fcf7d5e79bf
+size 17896232

README.md ADDED Viewed

	@@ -0,0 +1,170 @@

+---
+license: cc-by-4.0
+pretty_name: "SYTO v1.0 — data-driven soft labeling for whole-body cell-type deconvolution"
+viewer: false
+tags:
+  - biology
+  - genomics
+  - epigenomics
+  - dna-methylation
+  - cell-type-deconvolution
+  - read-classification
+---
+# SYTO v1.0 publication data
+Data underlying *"Data-driven soft labeling scales DNA read classification to
+whole-body cell-type deconvolution"*. SYTO is a framework for read-level DNA
+methylation classification and whole-body cell-type deconvolution. This deposit
+contains the trained models, generated pseudobulk mixtures, training data,
+source reads and final result tables behind every figure and table in the paper.
+> **This repository is a mirror.**
+> The canonical, citable version of record is TBD.
+> This mirror exists to make the data easier
+> to fetch programmatically; contents are identical to the planed citable deposit.
+- **Size:** 190.4 GB across 89 files (84 `.zip` archives + 4 acompanying metadata files + 1 csv file with Syto variants and baselines ranking)
+- **Unpacked:** 82,002 files
+- **Largest file:** 5.66 GB
+## Contents
+The deposit is organised in five tiers of decreasing necessity, so you can take
+only the depth you need.
+| Tier | Contents | Size |
+|---|---|---|
+| `tier0-results/` | Final result tables underlying important published figures and tables | 0.5 GB |
+| `tier1-models/` | Trained classifiers, deconvolvers and calibrators | 24.0 GB |
+| `tier2-pseudobulks/` | Generated pseudobulk mixtures (columnar Parquet) | 141.5 GB |
+| `tier3-training-data/` | Marker atlases, target proportions, training datasets | 12.6 GB |
+| `tier4-source-data/` | Staged and recovered reads, hg19/hg38 reference genomes | 11.8 GB |
+**`tier0-results/` alone (0.5 GB) is enough to inspect the final results of the paper.**
+Tiers 1–4 exist so the analysis can be independently re-run and verified.
+Metadata files are readable without downloading anything large:
+| File | Purpose |
+|---|---|
+| `MANIFEST.csv` | One row per archive: description, size, file count, SHA-256 |
+| `MANIFEST_CONTENTS.csv` | One row per file *inside* the archives — inspect an archive's contents without downloading it |
+| `runs.csv` | One row per experiment run, linking tier1 ↔ tier2 ↔ tier3 paths |
+| `FILE_NAMING_CONVENTION.txt` | Full naming scheme and every abbreviation used |
+## Downloading
+Archives are stored as-is; there is no dataset viewer. Fetch selectively.
+Inspect what exists first — both manifests are small:
+```python
+import pandas as pd
+pd.read_csv("hf://datasets/CompEpigen/syto.1.0/MANIFEST.csv")
+pd.read_csv("hf://datasets/CompEpigen/syto.1.0/MANIFEST_CONTENTS.csv")
+```
+Just the results (0.5 GB):
+```bash
+hf download CompEpigen/syto.1.0 --repo-type=dataset \
+  --include "tier0-results/*" "*.csv" "*.txt" --local-dir ./syto-data
+```
+One specific run:
+```bash
+hf download CompEpigen/syto.1.0 --repo-type=dataset \
+  --include "tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip" \
+  --local-dir ./syto-data
+```
+Everything (190 GB):
+```bash
+hf download CompEpigen/syto.1.0 --repo-type=dataset --local-dir ./syto-data
+```
+## Unpacking
+**Always unpack from the deposit root.** Archive members are stored with paths
+relative to the root, so unpacking an archive from inside its own folder nests
+the tree a second time (`tier1-models/ood-rrbs/tier1-models/...`)
+```bash
+cd ./syto
+find . -name 'SYTO_*.zip' -exec unzip -o -q {} ';'
+```
+The reference-genome archives contain relative symlinks
+(`genome.fa.gz -> hg38.fa.gz`) matching the `wgbs_tools` layout. Unpack them
+with a tool that preserves symlinks — `unzip` on Linux and macOS does.
+## Verifying
+`MANIFEST.csv` carries a SHA-256 for every archive:
+```bash
+python - <<'PY'
+import csv, hashlib, pathlib
+for r in csv.DictReader(open("MANIFEST.csv")):
+    p = pathlib.Path(r["item"])
+    if not p.exists():
+        continue
+    h = hashlib.sha256()
+    with p.open("rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+    print("OK " if h.hexdigest() == r["sha256"] else "BAD", p)
+PY
+```
+## Naming convention
+See `FILE_NAMING_CONVENTION.txt` for the complete scheme, including feature
+sets, atlas names and reference-genome codes.
+## What is not included
+Redundant, regenerable and training-only artefacts were removed before deposit:
+duplicated inputs, per-calibrator predictions
+regenerable from the calibrator plus features, optimizer states, intermediates
+superseded by the deposited aggregate tables, and diagnostic plots not part of
+the published results.
+Two inputs are referenced by the published configs but not redistributed here,
+marked by placeholders:
+- `${LOYFER_RECOVERED_READS}` — Loyfer recovered read tables; outputs of
+  [CompEpigen/wgbs_atlas_simulation](https://github.com/CompEpigen/wgbs_atlas_simulation)
+- `${SYTO_MLFLOW}` — an intermediate store for the `*_predicted.pkl` files
+  (data splits enriched with trained-classifier outputs, used as pseudobulk
+  inputs). The pseudobulk pipeline runs in `predictions_only` mode to produce
+  them; two-stage configs are included where relevant.
+Experiments were originally recorded in a local MLflow registry. MLflow-specific
+files were stripped for release, but each `tier1-models` element retains its
+recorded metrics, parameters and tags.
+**A note on configs:** published YAML configs use paths relative to the deposit
+root and were automatically redacted from their original HPC paths. Experiments
+were not re-run after repackaging, so treat the configs as authoritative for
+*parameters* while expecting to adjust *paths* to match your unpacking layout.
+## Citation
+```bibtex
+@article{rizdvanetskyi2026data,
+  title={Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution},
+  author={Rizdvanetskyi, Dmytro and Roos, Nathan and Lutsik, Pavlo},
+  journal={arXiv preprint arXiv:2607.04987},
+  year={2026}
+}
+```
+Thee dataset DOI is TBD.
+## License
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

runs.csv ADDED Viewed

	@@ -0,0 +1,36 @@

+run_id,experiment,classifier,labeling_sceme,num_labels,min_pattern_length,has_pseudobulks,has_deconvolution,has_calibration,has_ood_inference,tier1_path,tier2_path,training_regions,inference_regions,training_dataset_path
+5b0cc3ebb14d40788b11978a691f41f8,pseudobulk,Dismir,hard_labels_with_background,40,4,true,true,true,false,tier1-models/pseudobulk/dismir__hard_label_with_background,tier2-pseudobulks/pseudobulk/dismir__hard_label_with_background,tier3-training-data/atlases/Atlas.U25.l4.hg38.full.tsv,tier3-training-data/atlases/Atlas.U25.l4.hg38.full.tsv,tier3-training-data/datasets/hg38/pseudobulk-train-labels
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+Classifier	Labeling Scheme	Prior	Feature Scheme	Deconvolver	Calibrator	method	method_key	pseudobulk_r2	tcs	rank_pseudobulk	rank_tcs	avg_rank
+methylbert	soft_labels_with_pooling		top156	psls	linear_simplex_projection	methylbert / soft_labels_with_pooling / top156 / psls / linear_simplex_projection	methylbert|soft_labels_with_pooling||top156|psls|linear_simplex_projection	0.9889460568421344	0.7123821700160654	6	27	17
+methylbert	soft_labels_with_pooling		top156	swn	linear_simplex_projection	methylbert / soft_labels_with_pooling / top156 / swn / linear_simplex_projection	methylbert|soft_labels_with_pooling||top156|swn|linear_simplex_projection	0.9849812605861028	0.7249655177910103	35	10	23
+methylbert	soft_labels_with_pooling		top156	swn	linear_clip_normalize	methylbert / soft_labels_with_pooling / top156 / swn / linear_clip_normalize	methylbert|soft_labels_with_pooling||top156|swn|linear_clip_normalize	0.9859499421023464	0.7129535363514738	26	23	25
+methylbert	soft_labels_with_pooling		top156	swn	vector_scaling	methylbert / soft_labels_with_pooling / top156 / swn / vector_scaling	methylbert|soft_labels_with_pooling||top156|swn|vector_scaling	0.9837954924267952	0.7247388694619842	51	11	31
+methylbert	soft_labels_with_pooling		top156	nnls	linear_simplex_projection	methylbert / soft_labels_with_pooling / top156 / nnls / linear_simplex_projection	methylbert|soft_labels_with_pooling||top156|nnls|linear_simplex_projection	0.9893797481101504	0.6960109317498391	1	64	33
+dismir	hard_labels		top156	nnls	linear_simplex_projection	dismir / hard_labels / top156 / nnls / linear_simplex_projection	dismir|hard_labels||top156|nnls|linear_simplex_projection	0.9868485685376084	0.6986625865471543	17	59	38
+dismir	soft_labels_without_pooling		top156	swn	linear_clip_normalize	dismir / soft_labels_without_pooling / top156 / swn / linear_clip_normalize	dismir|soft_labels_without_pooling||top156|swn|linear_clip_normalize	0.9841781135562466	0.7106805385291166	45	32	39
+methylbert	soft_labels_with_pooling		top156	swn	uncalibrated	methylbert / soft_labels_with_pooling / top156 / swn / uncalibrated	methylbert|soft_labels_with_pooling||top156|swn|uncalibrated	0.984948289629694	0.7070784457064805	36	42	39
+methylbert	soft_labels_with_pooling		top156	mlp	linear_simplex_projection	methylbert / soft_labels_with_pooling / top156 / mlp / linear_simplex_projection	methylbert|soft_labels_with_pooling||top156|mlp|linear_simplex_projection	0.9852368929266168	0.6990076999905004	32	58	45
+dismir	soft_labels_without_pooling		top156	swn	linear_simplex_projection	dismir / soft_labels_without_pooling / top156 / swn / linear_simplex_projection	dismir|soft_labels_without_pooling||top156|swn|linear_simplex_projection	0.9836535123455566	0.710351101787718	57	34	46
+dismir	soft_labels_without_pooling		top156	nnls	linear_simplex_projection	dismir / soft_labels_without_pooling / top156 / nnls / linear_simplex_projection	dismir|soft_labels_without_pooling||top156|nnls|linear_simplex_projection	0.9889154605791995	0.6878419654377618	7	87	47
+dismir	soft_labels_without_pooling		top156	psls	linear_simplex_projection	dismir / soft_labels_without_pooling / top156 / psls / linear_simplex_projection	dismir|soft_labels_without_pooling||top156|psls|linear_simplex_projection	0.9887841368623989	0.6849057868330028	8	93	51
+dismir	soft_labels_without_pooling		top156	swn	vector_scaling	dismir / soft_labels_without_pooling / top156 / swn / vector_scaling	dismir|soft_labels_without_pooling||top156|swn|vector_scaling	0.982872426127438	0.7126930106627609	76	25	51
+dismir	soft_labels_without_pooling		top156	swn	uncalibrated	dismir / soft_labels_without_pooling / top156 / swn / uncalibrated	dismir|soft_labels_without_pooling||top156|swn|uncalibrated	0.982377574272848	0.7132983962140551	84	22	53
+cancerdetector		train_freq_prior	top156	mlp	linear_simplex_projection	cancerdetector / train_freq_prior / top156 / mlp / linear_simplex_projection	cancerdetector||train_freq_prior|top156|mlp|linear_simplex_projection	0.9828124201171644	0.7121625413638448	79	29	54
+cancerdetector		train_freq_prior	top156	swn	vector_scaling	cancerdetector / train_freq_prior / top156 / swn / vector_scaling	cancerdetector||train_freq_prior|top156|swn|vector_scaling	0.9832055782196714	0.705622812597497	64	45	55
+cancerdetector		train_freq_prior	top156	swn	linear_simplex_projection	cancerdetector / train_freq_prior / top156 / swn / linear_simplex_projection	cancerdetector||train_freq_prior|top156|swn|linear_simplex_projection	0.983689885239182	0.701142929506541	56	54	55
+cancerdetector		train_freq_prior	top156	mlp	vector_scaling	cancerdetector / train_freq_prior / top156 / mlp / vector_scaling	cancerdetector||train_freq_prior|top156|mlp|vector_scaling	0.9828818738250352	0.7102737142639475	75	35	55
+methylbert	soft_labels_with_pooling		top156	psls	linear_clip_normalize	methylbert / soft_labels_with_pooling / top156 / psls / linear_clip_normalize	methylbert|soft_labels_with_pooling||top156|psls|linear_clip_normalize	0.986105276492809	0.687050291341878	25	89	57
+methylbert	soft_labels_with_pooling		top156	mlp	vector_scaling	methylbert / soft_labels_with_pooling / top156 / mlp / vector_scaling	methylbert|soft_labels_with_pooling||top156|mlp|vector_scaling	0.9846232754470337	0.6935521795428772	40	74	57
+cancerdetector		train_freq_prior	top156	nnls	linear_simplex_projection	cancerdetector / train_freq_prior / top156 / nnls / linear_simplex_projection	cancerdetector||train_freq_prior|top156|nnls|linear_simplex_projection	0.987350519490976	0.680367774601671	10	106	58
+dismir	soft_labels_without_pooling		top156	nnls	linear_clip_normalize	dismir / soft_labels_without_pooling / top156 / nnls / linear_clip_normalize	dismir|soft_labels_without_pooling||top156|nnls|linear_clip_normalize	0.9858092080687354	0.6844412666035502	27	95	61
+dismir	hard_labels		top156	nnls	linear_clip_normalize	dismir / hard_labels / top156 / nnls / linear_clip_normalize	dismir|hard_labels||top156|nnls|linear_clip_normalize	0.9835807218534274	0.6967781482933035	60	62	61
+methylbert	soft_labels_with_pooling		top156	nnls	vector_scaling	methylbert / soft_labels_with_pooling / top156 / nnls / vector_scaling	methylbert|soft_labels_with_pooling||top156|nnls|vector_scaling	0.9818302664893322	0.711851707357511	93	31	62
+methylbert	soft_labels_without_pooling		top156	psls	linear_simplex_projection	methylbert / soft_labels_without_pooling / top156 / psls / linear_simplex_projection	methylbert|soft_labels_without_pooling||top156|psls|linear_simplex_projection	0.986159372696786	0.6808805615882352	24	105	65
+dismir	soft_labels_without_pooling		top156	psls	linear_clip_normalize	dismir / soft_labels_without_pooling / top156 / psls / linear_clip_normalize	dismir|soft_labels_without_pooling||top156|psls|linear_clip_normalize	0.9856631476970092	0.6815175252333353	29	102	66
+dismir	soft_labels_without_pooling		top156	mlp	vector_scaling	dismir / soft_labels_without_pooling / top156 / mlp / vector_scaling	dismir|soft_labels_without_pooling||top156|mlp|vector_scaling	0.9831470412789792	0.6960772846712647	68	63	66
+methylbert	soft_labels_with_pooling		top156	psls	vector_scaling	methylbert / soft_labels_with_pooling / top156 / psls / vector_scaling	methylbert|soft_labels_with_pooling||top156|psls|vector_scaling	0.9807113683565536	0.7162803067930814	112	19	66
+methylbert	soft_labels_with_pooling		top156	mlp	linear_clip_normalize	methylbert / soft_labels_with_pooling / top156 / mlp / linear_clip_normalize	methylbert|soft_labels_with_pooling||top156|mlp|linear_clip_normalize	0.9843897694680924	0.686273511572954	42	91	67
+dismir	soft_labels_without_pooling		top156	nnls	vector_scaling	dismir / soft_labels_without_pooling / top156 / nnls / vector_scaling	dismir|soft_labels_without_pooling||top156|nnls|vector_scaling	0.9832634395494776	0.6926011955593835	62	77	70
+cancerdetector		train_freq_prior	top156	psls	linear_simplex_projection	cancerdetector / train_freq_prior / top156 / psls / linear_simplex_projection	cancerdetector||train_freq_prior|top156|psls|linear_simplex_projection	0.9890178950463075	0.6703633513070539	5	135	70
+dismir	soft_labels_with_pooling		top156	swn	linear_clip_normalize	dismir / soft_labels_with_pooling / top156 / swn / linear_clip_normalize	dismir|soft_labels_with_pooling||top156|swn|linear_clip_normalize	0.9852290863402268	0.6793922260344323	33	108	71
+dismir	hard_labels		top156	swn	linear_clip_normalize	dismir / hard_labels / top156 / swn / linear_clip_normalize	dismir|hard_labels||top156|swn|linear_clip_normalize	0.9811902220699448	0.7097373891726142	106	36	71
+cancerdetector		uniform_prior	top156	xgb	linear_simplex_projection	cancerdetector / uniform_prior / top156 / xgb / linear_simplex_projection	cancerdetector||uniform_prior|top156|xgb|linear_simplex_projection	0.9798343417460448	0.7266449019994848	134	8	71
+methylbert	soft_labels_with_pooling		top156	nnls	linear_clip_normalize	methylbert / soft_labels_with_pooling / top156 / nnls / linear_clip_normalize	methylbert|soft_labels_with_pooling||top156|nnls|linear_clip_normalize	0.9869778774295486	0.6732129714840747	15	128	72
+dismir	soft_labels_with_pooling		top156	swn	linear_simplex_projection	dismir / soft_labels_with_pooling / top156 / swn / linear_simplex_projection	dismir|soft_labels_with_pooling||top156|swn|linear_simplex_projection	0.9850257506909972	0.6790194425399453	34	109	72
+cancerdetector		uniform_prior	top156	swn	vector_scaling	cancerdetector / uniform_prior / top156 / swn / vector_scaling	cancerdetector||uniform_prior|top156|swn|vector_scaling	0.9833298389001331	0.6893681069051981	61	83	72
+cancerdetector		train_freq_prior	top156	xgb	linear_simplex_projection	cancerdetector / train_freq_prior / top156 / xgb / linear_simplex_projection	cancerdetector||train_freq_prior|top156|xgb|linear_simplex_projection	0.979632501947134	0.7434190065379813	141	6	74
+cancerdetector		train_freq_prior	top156	mlp	linear_clip_normalize	cancerdetector / train_freq_prior / top156 / mlp / linear_clip_normalize	cancerdetector||train_freq_prior|top156|mlp|linear_clip_normalize	0.981660000507572	0.701797398542555	95	53	74
+dismir	soft_labels_with_pooling		top156	swn	vector_scaling	dismir / soft_labels_with_pooling / top156 / swn / vector_scaling	dismir|soft_labels_with_pooling||top156|swn|vector_scaling	0.9837692930958752	0.6825836810657926	52	100	76
+cancerdetector		train_freq_prior	top156	nnls	vector_scaling	cancerdetector / train_freq_prior / top156 / nnls / vector_scaling	cancerdetector||train_freq_prior|top156|nnls|vector_scaling	0.9810469193165378	0.7045942606679875	107	46	77
+cancerdetector		train_freq_prior	top156	swn	linear_clip_normalize	cancerdetector / train_freq_prior / top156 / swn / linear_clip_normalize	cancerdetector||train_freq_prior|top156|swn|linear_clip_normalize	0.9829437743455788	0.6891716163047088	72	84	78
+cancerdetector		uniform_prior	top156	nnls	vector_scaling	cancerdetector / uniform_prior / top156 / nnls / vector_scaling	cancerdetector||uniform_prior|top156|nnls|vector_scaling	0.9814413717183622	0.6977102491006516	97	60	79
+cancerdetector		uniform_prior	top156	nnls	linear_simplex_projection	cancerdetector / uniform_prior / top156 / nnls / linear_simplex_projection	cancerdetector||uniform_prior|top156|nnls|linear_simplex_projection	0.9863738891374152	0.6703546347969845	22	136	79
+methylbert	soft_labels_without_pooling		top156	nnls	linear_simplex_projection	methylbert / soft_labels_without_pooling / top156 / nnls / linear_simplex_projection	methylbert|soft_labels_without_pooling||top156|nnls|linear_simplex_projection	0.9847556186856392	0.6753600813966922	38	122	80
+cancerdetector		uniform_prior	top156	swn	linear_simplex_projection	cancerdetector / uniform_prior / top156 / swn / linear_simplex_projection	cancerdetector||uniform_prior|top156|swn|linear_simplex_projection	0.9835916154749578	0.6812156243816571	59	103	81
+dismir	hard_labels		top156	swn	uncalibrated	dismir / hard_labels / top156 / swn / uncalibrated	dismir|hard_labels||top156|swn|uncalibrated	0.9797723909074189	0.7122726267562383	137	28	83
+dismir	soft_labels_with_pooling		top156	swn	uncalibrated	dismir / soft_labels_with_pooling / top156 / swn / uncalibrated	dismir|soft_labels_with_pooling||top156|swn|uncalibrated	0.9831518896307424	0.6817703481230568	67	101	84
+dismir	hard_labels		top156	swn	linear_simplex_projection	dismir / hard_labels / top156 / swn / linear_simplex_projection	dismir|hard_labels||top156|swn|linear_simplex_projection	0.9799322848818434	0.7095915379304734	130	38	84
+dismir	soft_labels_without_pooling		top156	psls	vector_scaling	dismir / soft_labels_without_pooling / top156 / psls / vector_scaling	dismir|soft_labels_without_pooling||top156|psls|vector_scaling	0.9822944495832155	0.6870671558652415	85	88	87
+dismir	hard_labels		top156	nnls	vector_scaling	dismir / hard_labels / top156 / nnls / vector_scaling	dismir|hard_labels||top156|nnls|vector_scaling	0.9788966544936244	0.7172651565577153	155	18	87
+cancerdetector		uniform_prior	top156	psls	vector_scaling	cancerdetector / uniform_prior / top156 / psls / vector_scaling	cancerdetector||uniform_prior|top156|psls|vector_scaling	0.984580310494928	0.6707232403506403	41	134	88
+dismir	hard_labels		diagbckg	psls	linear_simplex_projection	dismir / hard_labels / diagbckg / psls / linear_simplex_projection	dismir|hard_labels||diagbckg|psls|linear_simplex_projection	0.9869943425098922	0.6649849528307514	13	164	89
+cancerdetector		train_freq_prior	top156	psls	vector_scaling	cancerdetector / train_freq_prior / top156 / psls / vector_scaling	cancerdetector||train_freq_prior|top156|psls|vector_scaling	0.9836001700093824	0.6756306051719915	58	119	89
+dismir	hard_labels		top156	psls	linear_simplex_projection	dismir / hard_labels / top156 / psls / linear_simplex_projection	dismir|hard_labels||top156|psls|linear_simplex_projection	0.9869892145877386	0.6649751037272349	14	165	90
+dismir	soft_labels_without_pooling		top156	mlp	linear_simplex_projection	dismir / soft_labels_without_pooling / top156 / mlp / linear_simplex_projection	dismir|soft_labels_without_pooling||top156|mlp|linear_simplex_projection	0.9812424391721184	0.6925236185034693	103	78	91
+lookup	soft_labels_with_pooling		top156	psls	linear_simplex_projection	lookup / soft_labels_with_pooling / top156 / psls / linear_simplex_projection	lookup|soft_labels_with_pooling||top156|psls|linear_simplex_projection	0.9826997913401808	0.681118549776542	80	104	92
+methylbert	soft_labels_without_pooling		top156	swn	linear_simplex_projection	methylbert / soft_labels_without_pooling / top156 / swn / linear_simplex_projection	methylbert|soft_labels_without_pooling||top156|swn|linear_simplex_projection	0.9820231859008456	0.6857475295076899	92	92	92
+dismir	soft_labels_with_pooling		top156	psls	linear_simplex_projection	dismir / soft_labels_with_pooling / top156 / psls / linear_simplex_projection	dismir|soft_labels_with_pooling||top156|psls|linear_simplex_projection	0.9891630590482507	0.6564182801828252	3	189	96
+dismir	hard_labels		diagbckg	nnls	linear_simplex_projection	dismir / hard_labels / diagbckg / nnls / linear_simplex_projection	dismir|hard_labels||diagbckg|nnls|linear_simplex_projection	0.987257017059692	0.6583739473098351	11	181	96
+methylbert	soft_labels_without_pooling		top156	swn	vector_scaling	methylbert / soft_labels_without_pooling / top156 / swn / vector_scaling	methylbert|soft_labels_without_pooling||top156|swn|vector_scaling	0.981658490076706	0.6844006665632985	96	96	96
+dismir	soft_labels_without_pooling		top156	nnls	uncalibrated	dismir / soft_labels_without_pooling / top156 / nnls / uncalibrated	dismir|soft_labels_without_pooling||top156|nnls|uncalibrated	0.9813013973861524	0.6864645017775176	102	90	96
+cancerdetector		uniform_prior	top156	psls	linear_simplex_projection	cancerdetector / uniform_prior / top156 / psls / linear_simplex_projection	cancerdetector||uniform_prior|top156|psls|linear_simplex_projection	0.9890876453238968	0.6563678551537846	4	190	97
+dismir	hard_labels		diagbckg	swn	linear_clip_normalize	dismir / hard_labels / diagbckg / swn / linear_clip_normalize	dismir|hard_labels||diagbckg|swn|linear_clip_normalize	0.9804430237147964	0.6927832050857812	119	76	98
+dismir	hard_labels		top156	swn	vector_scaling	dismir / hard_labels / top156 / swn / vector_scaling	dismir|hard_labels||top156|swn|vector_scaling	0.9779629343288112	0.7125569227677906	169	26	98
+Celfie					linear_simplex_projection	Celfie / linear_simplex_projection	celfie|||||linear_simplex_projection	0.9803476202961432	0.6936058209151715	124	73	99
+cancerdetector		uniform_prior	top156	xgb	vector_scaling	cancerdetector / uniform_prior / top156 / xgb / vector_scaling	cancerdetector||uniform_prior|top156|xgb|vector_scaling	0.9768027073176336	0.7343327997852893	191	7	99
+methylbert	soft_labels_without_pooling		top156	psls	vector_scaling	methylbert / soft_labels_without_pooling / top156 / psls / vector_scaling	methylbert|soft_labels_without_pooling||top156|psls|vector_scaling	0.9799297714803068	0.6950720073858603	131	69	100
+dismir	soft_labels_without_pooling		top156	xgb	linear_simplex_projection	dismir / soft_labels_without_pooling / top156 / xgb / linear_simplex_projection	dismir|soft_labels_without_pooling||top156|xgb|linear_simplex_projection	0.9770935328615714	0.7215117715586513	187	14	101
+methylbert	soft_labels_with_pooling		top156	mlp	uncalibrated	methylbert / soft_labels_with_pooling / top156 / mlp / uncalibrated	methylbert|soft_labels_with_pooling||top156|mlp|uncalibrated	0.982221558205212	0.6770168640297656	88	114	101
+lookup	soft_labels_with_pooling		top156	nnls	linear_simplex_projection	lookup / soft_labels_with_pooling / top156 / nnls / linear_simplex_projection	lookup|soft_labels_with_pooling||top156|nnls|linear_simplex_projection	0.981216342704616	0.6842811698151092	104	98	101
+cancerdetector		train_freq_prior	top156	xgb	vector_scaling	cancerdetector / train_freq_prior / top156 / xgb / vector_scaling	cancerdetector||train_freq_prior|top156|xgb|vector_scaling	0.9760427000261344	0.7513263165309906	203	4	104
+cancerdetector		train_freq_prior	top156	xgb	linear_clip_normalize	cancerdetector / train_freq_prior / top156 / xgb / linear_clip_normalize	cancerdetector||train_freq_prior|top156|xgb|linear_clip_normalize	0.9766338013697982	0.7231077500335187	196	12	104
+cancerdetector		uniform_prior	top156	swn	linear_clip_normalize	cancerdetector / uniform_prior / top156 / swn / linear_clip_normalize	cancerdetector||uniform_prior|top156|swn|linear_clip_normalize	0.9830785391673836	0.6701383431598213	70	139	105
+methylbert	soft_labels_without_pooling		top156	nnls	vector_scaling	methylbert / soft_labels_without_pooling / top156 / nnls / vector_scaling	methylbert|soft_labels_without_pooling||top156|nnls|vector_scaling	0.9796782871031138	0.6947570210652172	140	70	105
+dismir	soft_labels_with_pooling		top156	nnls	linear_simplex_projection	dismir / soft_labels_with_pooling / top156 / nnls / linear_simplex_projection	dismir|soft_labels_with_pooling||top156|nnls|linear_simplex_projection	0.989289450219986	0.6442277368567579	2	209	106
+methylbert	soft_labels_without_pooling		top156	swn	linear_clip_normalize	methylbert / soft_labels_without_pooling / top156 / swn / linear_clip_normalize	methylbert|soft_labels_without_pooling||top156|swn|linear_clip_normalize	0.9820920925346164	0.6756062720785508	91	120	106
+cancerdetector		uniform_prior	top156	mlp	vector_scaling	cancerdetector / uniform_prior / top156 / mlp / vector_scaling	cancerdetector||uniform_prior|top156|mlp|vector_scaling	0.9804754700434992	0.6847091454603098	118	94	106
+cancerdetector		train_freq_prior	top156	psls	linear_clip_normalize	cancerdetector / train_freq_prior / top156 / psls / linear_clip_normalize	cancerdetector||train_freq_prior|top156|psls|linear_clip_normalize	0.9868087447454158	0.6550901208111577	19	194	107
+lookup	soft_labels_with_pooling		top156	psls	vector_scaling	lookup / soft_labels_with_pooling / top156 / psls / vector_scaling	lookup|soft_labels_with_pooling||top156|psls|vector_scaling	0.9773333061051004	0.7103595079306159	181	33	107
+dismir	soft_labels_with_pooling		top156	psls	linear_clip_normalize	dismir / soft_labels_with_pooling / top156 / psls / linear_clip_normalize	dismir|soft_labels_with_pooling||top156|psls|linear_clip_normalize	0.9862724891764136	0.6543865521488783	23	195	109
+dismir	hard_labels		top156	nnls	uncalibrated	dismir / hard_labels / top156 / nnls / uncalibrated	dismir|hard_labels||top156|nnls|uncalibrated	0.9786096154394216	0.6975560488583235	157	61	109
+dismir	soft_labels_without_pooling		top156	mlp	linear_clip_normalize	dismir / soft_labels_without_pooling / top156 / mlp / linear_clip_normalize	dismir|soft_labels_without_pooling||top156|mlp|linear_clip_normalize	0.9797018215182416	0.6909421196971394	138	81	110
+lookup	soft_labels_with_pooling		top156	nnls	vector_scaling	lookup / soft_labels_with_pooling / top156 / nnls / vector_scaling	lookup|soft_labels_with_pooling||top156|nnls|vector_scaling	0.9765698612591258	0.7133779644656215	198	21	110
+dismir	hard_labels		diagbckg	swn	linear_simplex_projection	dismir / hard_labels / diagbckg / swn / linear_simplex_projection	dismir|hard_labels||diagbckg|swn|linear_simplex_projection	0.9794732836853818	0.6920570000852393	144	79	112
+cancerdetector		train_freq_prior	top156	mlp	uncalibrated	cancerdetector / train_freq_prior / top156 / mlp / uncalibrated	cancerdetector||train_freq_prior|top156|mlp|uncalibrated	0.979082433271408	0.6936114898550142	151	72	112
+dismir	soft_labels_without_pooling		top156	xgb	vector_scaling	dismir / soft_labels_without_pooling / top156 / xgb / vector_scaling	dismir|soft_labels_without_pooling||top156|xgb|vector_scaling	0.9750552420295132	0.7254349749590714	214	9	112
+dismir	soft_labels_without_pooling		top156	psls	uncalibrated	dismir / soft_labels_without_pooling / top156 / psls / uncalibrated	dismir|soft_labels_without_pooling||top156|psls|uncalibrated	0.9803274484511604	0.6834242317744672	125	99	112
+dismir	hard_labels		diagbckg	swn	uncalibrated	dismir / hard_labels / diagbckg / swn / uncalibrated	dismir|hard_labels||diagbckg|swn|uncalibrated	0.9786256617977156	0.6951156950753312	156	68	112
+dismir	hard_labels		top156	xgb	linear_simplex_projection	dismir / hard_labels / top156 / xgb / linear_simplex_projection	dismir|hard_labels||top156|xgb|linear_simplex_projection	0.9773103878220638	0.7073160972251501	184	41	113
+dismir	hard_labels		diagbckg	psls	linear_clip_normalize	dismir / hard_labels / diagbckg / psls / linear_clip_normalize	dismir|hard_labels||diagbckg|psls|linear_clip_normalize	0.983764952139134	0.6621274464228923	53	173	113
+dismir	hard_labels		top156	psls	linear_clip_normalize	dismir / hard_labels / top156 / psls / linear_clip_normalize	dismir|hard_labels||top156|psls|linear_clip_normalize	0.9837599251167772	0.6621166126258149	54	174	114
+cancerdetector		uniform_prior	top156	psls	linear_clip_normalize	cancerdetector / uniform_prior / top156 / psls / linear_clip_normalize	cancerdetector||uniform_prior|top156|psls|linear_clip_normalize	0.9868623675587782	0.641340431095511	16	215	116
+cancerdetector		train_freq_prior	top156	nnls	linear_clip_normalize	cancerdetector / train_freq_prior / top156 / nnls / linear_clip_normalize	cancerdetector||train_freq_prior|top156|nnls|linear_clip_normalize	0.984040711745099	0.6571662704308798	48	184	116
+dismir	soft_labels_with_pooling		top156	nnls	linear_clip_normalize	dismir / soft_labels_with_pooling / top156 / nnls / linear_clip_normalize	dismir|soft_labels_with_pooling||top156|nnls|linear_clip_normalize	0.986384951948504	0.642348810234722	21	213	117
+dismir	hard_labels		top156	xgb	vector_scaling	dismir / hard_labels / top156 / xgb / vector_scaling	dismir|hard_labels||top156|xgb|vector_scaling	0.9760195854400396	0.7119998123344566	204	30	117
+lookup	soft_labels_with_pooling		top156	swn	vector_scaling	lookup / soft_labels_with_pooling / top156 / swn / vector_scaling	lookup|soft_labels_with_pooling||top156|swn|vector_scaling	0.9773286987460434	0.7021958488902637	183	52	118
+cancerdetector		uniform_prior	top156	xgb	linear_clip_normalize	cancerdetector / uniform_prior / top156 / xgb / linear_clip_normalize	cancerdetector||uniform_prior|top156|xgb|linear_clip_normalize	0.9766659997653994	0.7079071674744601	195	40	118
+dismir	hard_labels		diagbckg	nnls	linear_clip_normalize	dismir / hard_labels / diagbckg / nnls / linear_clip_normalize	dismir|hard_labels||diagbckg|nnls|linear_clip_normalize	0.9841770760545284	0.6560648513146908	46	191	119
+dismir	canonical_soft_labels		diagbckg	xgb	linear_simplex_projection	dismir / canonical_soft_labels / diagbckg / xgb / linear_simplex_projection	dismir|canonical_soft_labels||diagbckg|xgb|linear_simplex_projection	0.9775590426722108	0.6958977880897396	174	65	120
+dismir	soft_labels_without_pooling		top156	xgb	linear_clip_normalize	dismir / soft_labels_without_pooling / top156 / xgb / linear_clip_normalize	dismir|soft_labels_without_pooling||top156|xgb|linear_clip_normalize	0.9739834016169916	0.7212546960796313	225	15	120
+methylbert	soft_labels_without_pooling		top156	psls	linear_clip_normalize	methylbert / soft_labels_without_pooling / top156 / psls / linear_clip_normalize	methylbert|soft_labels_without_pooling||top156|psls|linear_clip_normalize	0.982559297000932	0.6661310099478934	82	160	121
+dismir	hard_labels		diagbckg	swn	vector_scaling	dismir / hard_labels / diagbckg / swn / vector_scaling	dismir|hard_labels||diagbckg|swn|vector_scaling	0.9778444595289544	0.6943264381855573	172	71	122
+dismir	hard_labels		diagbckg	xgb	linear_simplex_projection	dismir / hard_labels / diagbckg / xgb / linear_simplex_projection	dismir|hard_labels||diagbckg|xgb|linear_simplex_projection	0.9765897981824097	0.7037961046350992	197	47	122
+dismir	soft_labels_with_pooling		top156	nnls	vector_scaling	dismir / soft_labels_with_pooling / top156 / nnls / vector_scaling	dismir|soft_labels_with_pooling||top156|nnls|vector_scaling	0.9838973132214656	0.6542890149270473	50	196	123
+methylbert	canonical_soft_labels		top156	psls	linear_simplex_projection	methylbert / canonical_soft_labels / top156 / psls / linear_simplex_projection	methylbert|canonical_soft_labels||top156|psls|linear_simplex_projection	0.9876851201881738	0.6176072220370314	9	238	124
+cancerdetector		train_freq_prior	top156	psls	uncalibrated	cancerdetector / train_freq_prior / top156 / psls / uncalibrated	cancerdetector||train_freq_prior|top156|psls|uncalibrated	0.9841311782241052	0.6530689025169166	47	200	124
+dismir	soft_labels_with_pooling		top156	psls	vector_scaling	dismir / soft_labels_with_pooling / top156 / psls / vector_scaling	dismir|soft_labels_with_pooling||top156|psls|vector_scaling	0.9825844964685124	0.6637410467717538	81	169	125
+cancerdetector		train_freq_prior	top156	swn	uncalibrated	cancerdetector / train_freq_prior / top156 / swn / uncalibrated	cancerdetector||train_freq_prior|top156|swn|uncalibrated	0.9802211467677368	0.6752821747845923	127	123	125
+methylbert	soft_labels_with_pooling		top156	psls	uncalibrated	methylbert / soft_labels_with_pooling / top156 / psls / uncalibrated	methylbert|soft_labels_with_pooling||top156|psls|uncalibrated	0.9798861183349042	0.6758383419938149	132	118	125
+cancerdetector		uniform_prior	top156	mlp	linear_simplex_projection	cancerdetector / uniform_prior / top156 / mlp / linear_simplex_projection	cancerdetector||uniform_prior|top156|mlp|linear_simplex_projection	0.9802740854956724	0.6743315963619897	126	126	126
+dismir	hard_labels		diagbckg	xgb	vector_scaling	dismir / hard_labels / diagbckg / xgb / vector_scaling	dismir|hard_labels||diagbckg|xgb|vector_scaling	0.9750802694776136	0.7084710551124028	213	39	126
+cancerdetector		train_freq_prior	top156	xgb	uncalibrated	cancerdetector / train_freq_prior / top156 / xgb / uncalibrated	cancerdetector||train_freq_prior|top156|xgb|uncalibrated	0.9732355891473864	0.714414956312825	232	20	126
+dismir	soft_labels_with_pooling		top156	xgb	linear_simplex_projection	dismir / soft_labels_with_pooling / top156 / xgb / linear_simplex_projection	dismir|soft_labels_with_pooling||top156|xgb|linear_simplex_projection	0.9806680452291556	0.6690283422787259	113	145	129
+methylbert	canonical_soft_labels		top156	nnls	linear_simplex_projection	methylbert / canonical_soft_labels / top156 / nnls / linear_simplex_projection	methylbert|canonical_soft_labels||top156|nnls|linear_simplex_projection	0.9871204340063896	0.6111243094464621	12	247	130
+methylbert	soft_labels_with_pooling		top156	nnls	uncalibrated	methylbert / soft_labels_with_pooling / top156 / nnls / uncalibrated	methylbert|soft_labels_with_pooling||top156|nnls|uncalibrated	0.9816791395729584	0.6649128896179174	94	166	130
+lookup	soft_labels_with_pooling		top156	swn	linear_simplex_projection	lookup / soft_labels_with_pooling / top156 / swn / linear_simplex_projection	lookup|soft_labels_with_pooling||top156|swn|linear_simplex_projection	0.9774023998041356	0.6881704904958728	177	85	131
+dismir	soft_labels_without_pooling		top156	xgb	uncalibrated	dismir / soft_labels_without_pooling / top156 / xgb / uncalibrated	dismir|soft_labels_without_pooling||top156|xgb|uncalibrated	0.9708033620331116	0.720986597006923	247	16	132
+cancerdetector		uniform_prior	top156	psls	uncalibrated	cancerdetector / uniform_prior / top156 / psls / uncalibrated	cancerdetector||uniform_prior|top156|psls|uncalibrated	0.9843862743774644	0.6363427545801509	43	223	133
+dismir	canonical_soft_labels		diagbckg	xgb	vector_scaling	dismir / canonical_soft_labels / diagbckg / xgb / vector_scaling	dismir|canonical_soft_labels||diagbckg|xgb|vector_scaling	0.9751857294745768	0.7004907465039463	212	55	134
+dismir	hard_labels		top156	mlp	vector_scaling	dismir / hard_labels / top156 / mlp / vector_scaling	dismir|hard_labels||top156|mlp|vector_scaling	0.978940164418224	0.6767790978037843	154	115	135
+cancerdetector		uniform_prior	top156	nnls	linear_clip_normalize	cancerdetector / uniform_prior / top156 / nnls / linear_clip_normalize	cancerdetector||uniform_prior|top156|nnls|linear_clip_normalize	0.983154443917095	0.6476315026262078	66	205	136
+methylbert	soft_labels_without_pooling		top156	swn	uncalibrated	methylbert / soft_labels_without_pooling / top156 / swn / uncalibrated	methylbert|soft_labels_without_pooling||top156|swn|uncalibrated	0.9798672390073936	0.6696223262453173	133	141	137
+methylbert	soft_labels_with_pooling		top156	xgb	vector_scaling	methylbert / soft_labels_with_pooling / top156 / xgb / vector_scaling	methylbert|soft_labels_with_pooling||top156|xgb|vector_scaling	0.976450915096682	0.6933736096814853	199	75	137
+dismir	hard_labels		top156	xgb	linear_clip_normalize	dismir / hard_labels / top156 / xgb / linear_clip_normalize	dismir|hard_labels||top156|xgb|linear_clip_normalize	0.973610604084756	0.7068123605188965	231	43	137
+methylbert	soft_labels_with_pooling		top156	xgb	linear_simplex_projection	methylbert / soft_labels_with_pooling / top156 / xgb / linear_simplex_projection	methylbert|soft_labels_with_pooling||top156|xgb|linear_simplex_projection	0.980944122726126	0.6640366902358195	110	168	139
+dismir	soft_labels_without_pooling		top156	mlp	uncalibrated	dismir / soft_labels_without_pooling / top156 / mlp / uncalibrated	dismir|soft_labels_without_pooling||top156|mlp|uncalibrated	0.9764086488091552	0.6901217466455418	200	82	141
+dismir	canonical_soft_labels		top156	xgb	linear_simplex_projection	dismir / canonical_soft_labels / top156 / xgb / linear_simplex_projection	dismir|canonical_soft_labels||top156|xgb|linear_simplex_projection	0.9793386185128596	0.6702325180415744	146	137	142
+lookup	soft_labels_without_pooling		top156	nnls	linear_simplex_projection	lookup / soft_labels_without_pooling / top156 / nnls / linear_simplex_projection	lookup|soft_labels_without_pooling||top156|nnls|linear_simplex_projection	0.9667271546141532	0.7183075189043423	267	17	142
+dismir	hard_labels		diagbckg	xgb	linear_clip_normalize	dismir / hard_labels / diagbckg / xgb / linear_clip_normalize	dismir|hard_labels||diagbckg|xgb|linear_clip_normalize	0.9723504660456116	0.7033101797908973	238	48	143
+dismir	hard_labels		top156	mlp	linear_simplex_projection	dismir / hard_labels / top156 / mlp / linear_simplex_projection	dismir|hard_labels||top156|mlp|linear_simplex_projection	0.9785064306073984	0.6714110509871994	159	131	145
+cancerdetector		uniform_prior	top156	xgb	uncalibrated	cancerdetector / uniform_prior / top156 / xgb / uncalibrated	cancerdetector||uniform_prior|top156|xgb|uncalibrated	0.9731315402732034	0.6990493837423102	233	57	145
+lookup	soft_labels_with_pooling		top156	mlp	vector_scaling	lookup / soft_labels_with_pooling / top156 / mlp / vector_scaling	lookup|soft_labels_with_pooling||top156|mlp|vector_scaling	0.9754691905199364	0.6915648246992887	211	80	146
+dismir	soft_labels_with_pooling		top156	psls	uncalibrated	dismir / soft_labels_with_pooling / top156 / psls / uncalibrated	dismir|soft_labels_with_pooling||top156|psls|uncalibrated	0.9813715851970044	0.6557486594359956	100	192	146
+methylbert	canonical_soft_labels		top156	psls	linear_clip_normalize	methylbert / canonical_soft_labels / top156 / psls / linear_clip_normalize	methylbert|canonical_soft_labels||top156|psls|linear_clip_normalize	0.9849191774188348	0.6049219807491361	37	258	148
+dismir	canonical_soft_labels		diagbckg	xgb	linear_clip_normalize	dismir / canonical_soft_labels / diagbckg / xgb / linear_clip_normalize	dismir|canonical_soft_labels||diagbckg|xgb|linear_clip_normalize	0.9736809700843962	0.6955380210633877	229	66	148
+dismir	hard_labels		top156	xgb	uncalibrated	dismir / hard_labels / top156 / xgb / uncalibrated	dismir|hard_labels||top156|xgb|uncalibrated	0.9699191916349742	0.706525089059916	252	44	148
+dismir	soft_labels_with_pooling		top156	nnls	uncalibrated	dismir / soft_labels_with_pooling / top156 / nnls / uncalibrated	dismir|soft_labels_with_pooling||top156|nnls|uncalibrated	0.9822749314087466	0.6438097903366182	87	211	149
+Celfie					vector_scaling	Celfie / vector_scaling	celfie|||||vector_scaling	0.9635550868181862	0.7220204637654729	285	13	149
+methylbert	canonical_soft_labels		top156	swn	vector_scaling	methylbert / canonical_soft_labels / top156 / swn / vector_scaling	methylbert|canonical_soft_labels||top156|swn|vector_scaling	0.9821579724375664	0.6441188076975395	90	210	150
+methylbert	soft_labels_without_pooling		top156	nnls	linear_clip_normalize	methylbert / soft_labels_without_pooling / top156 / nnls / linear_clip_normalize	methylbert|soft_labels_without_pooling||top156|nnls|linear_clip_normalize	0.9804408533761014	0.6586523796358668	120	180	150
+methylbert	canonical_soft_labels		diagbckg	nnls	linear_simplex_projection	methylbert / canonical_soft_labels / diagbckg / nnls / linear_simplex_projection	methylbert|canonical_soft_labels||diagbckg|nnls|linear_simplex_projection	0.9868094402971151	0.5505825151259623	18	283	151
+methylbert	canonical_soft_labels		top156	nnls	vector_scaling	methylbert / canonical_soft_labels / top156 / nnls / vector_scaling	methylbert|canonical_soft_labels||top156|nnls|vector_scaling	0.9830870074970576	0.6264060172383212	69	234	152
+dismir	canonical_soft_labels		diagbckg	psls	linear_simplex_projection	dismir / canonical_soft_labels / diagbckg / psls / linear_simplex_projection	dismir|canonical_soft_labels||diagbckg|psls|linear_simplex_projection	0.9781010086608808	0.670151564523591	165	138	152
+cancerdetector		uniform_prior	top156	swn	uncalibrated	cancerdetector / uniform_prior / top156 / swn / uncalibrated	cancerdetector||uniform_prior|top156|swn|uncalibrated	0.9805007391709556	0.6567059254658996	117	187	152
+lookup	soft_labels_without_pooling		top156	nnls	vector_scaling	lookup / soft_labels_without_pooling / top156 / nnls / vector_scaling	lookup|soft_labels_without_pooling||top156|nnls|vector_scaling	0.9545518529588478	0.7727869481312041	303	1	152
+dismir	soft_labels_with_pooling		top156	xgb	vector_scaling	dismir / soft_labels_with_pooling / top156 / xgb / vector_scaling	dismir|soft_labels_with_pooling||top156|xgb|vector_scaling	0.9773790615357516	0.6736093333078064	180	127	154
+methylbert	canonical_soft_labels		top156	psls	vector_scaling	methylbert / canonical_soft_labels / top156 / psls / vector_scaling	methylbert|canonical_soft_labels||top156|psls|vector_scaling	0.982844896606184	0.6292697477008016	78	230	154
+dismir	hard_labels		diagbckg	xgb	uncalibrated	dismir / hard_labels / diagbckg / xgb / uncalibrated	dismir|hard_labels||diagbckg|xgb|uncalibrated	0.9682010458059002	0.7029891907313631	258	50	154
+cancerdetector		uniform_prior	top156	mlp	linear_clip_normalize	cancerdetector / uniform_prior / top156 / mlp / linear_clip_normalize	cancerdetector||uniform_prior|top156|mlp|linear_clip_normalize	0.979263830724825	0.6651766755448876	147	163	155
+dismir	hard_labels		top156	mlp	linear_clip_normalize	dismir / hard_labels / top156 / mlp / linear_clip_normalize	dismir|hard_labels||top156|mlp|linear_clip_normalize	0.977398819429422	0.6713344592436614	178	132	155
+lookup	soft_labels_without_pooling		top156	swn	vector_scaling	lookup / soft_labels_without_pooling / top156 / swn / vector_scaling	lookup|soft_labels_without_pooling||top156|swn|vector_scaling	0.951283992382522	0.7495925365242709	306	5	156
+lookup	soft_labels_without_pooling		top156	psls	vector_scaling	lookup / soft_labels_without_pooling / top156 / psls / vector_scaling	lookup|soft_labels_without_pooling||top156|psls|vector_scaling	0.9491287089053384	0.7638584499115328	309	2	156
+dismir	canonical_soft_labels		top156	mlp	linear_simplex_projection	dismir / canonical_soft_labels / top156 / mlp / linear_simplex_projection	dismir|canonical_soft_labels||top156|mlp|linear_simplex_projection	0.9794664069208232	0.663546415425269	145	170	158
+dismir	canonical_soft_labels		top156	nnls	linear_simplex_projection	dismir / canonical_soft_labels / top156 / nnls / linear_simplex_projection	dismir|canonical_soft_labels||top156|nnls|linear_simplex_projection	0.9761271359807264	0.6774909980459737	202	113	158
+dismir	canonical_soft_labels		top156	xgb	vector_scaling	dismir / canonical_soft_labels / top156 / xgb / vector_scaling	dismir|canonical_soft_labels||top156|xgb|vector_scaling	0.9767653328172508	0.6749427207783316	192	125	159
+cancerdetector		train_freq_prior	top156	nnls	uncalibrated	cancerdetector / train_freq_prior / top156 / nnls / uncalibrated	cancerdetector||train_freq_prior|top156|nnls|uncalibrated	0.9806589471247688	0.6482791729690589	114	204	159
+dismir	canonical_soft_labels		diagbckg	nnls	linear_simplex_projection	dismir / canonical_soft_labels / diagbckg / nnls / linear_simplex_projection	dismir|canonical_soft_labels||diagbckg|nnls|linear_simplex_projection	0.9797941366257849	0.657645863898668	136	182	159
+methylbert	canonical_soft_labels		top156	nnls	linear_clip_normalize	methylbert / canonical_soft_labels / top156 / nnls / linear_clip_normalize	methylbert|canonical_soft_labels||top156|nnls|linear_clip_normalize	0.9837247156394178	0.5934525774323025	55	265	160
+methylbert	canonical_soft_labels		diagbckg	psls	linear_simplex_projection	methylbert / canonical_soft_labels / diagbckg / psls / linear_simplex_projection	methylbert|canonical_soft_labels||diagbckg|psls|linear_simplex_projection	0.986601024308086	0.5111037646731131	20	301	161
+dismir	canonical_soft_labels		diagbckg	xgb	uncalibrated	dismir / canonical_soft_labels / diagbckg / xgb / uncalibrated	dismir|canonical_soft_labels||diagbckg|xgb|uncalibrated	0.9694516044598344	0.6951962165789081	254	67	161
+methylbert	hard_labels		top156	psls	linear_simplex_projection	methylbert / hard_labels / top156 / psls / linear_simplex_projection	methylbert|hard_labels||top156|psls|linear_simplex_projection	0.985412564321376	0.5257553671659034	30	292	161
+dismir	canonical_soft_labels		top156	nnls	vector_scaling	dismir / canonical_soft_labels / top156 / nnls / vector_scaling	dismir|canonical_soft_labels||top156|nnls|vector_scaling	0.9661020828716184	0.7026188192321847	272	51	162
+dismir	soft_labels_with_pooling		top156	xgb	linear_clip_normalize	dismir / soft_labels_with_pooling / top156 / xgb / linear_clip_normalize	dismir|soft_labels_with_pooling||top156|xgb|linear_clip_normalize	0.9774028420149322	0.6688940779418842	176	148	162
+methylbert	canonical_soft_labels		top156	swn	linear_simplex_projection	methylbert / canonical_soft_labels / top156 / swn / linear_simplex_projection	methylbert|canonical_soft_labels||top156|swn|linear_simplex_projection	0.9809928321035246	0.6404301931405051	109	216	163
+lookup	soft_labels_without_pooling		top156	mlp	vector_scaling	lookup / soft_labels_without_pooling / top156 / mlp / vector_scaling	lookup|soft_labels_without_pooling||top156|mlp|vector_scaling	0.9271902958941048	0.7544008261887544	322	2	163
+methylbert	canonical_soft_labels		top156	swn	linear_clip_normalize	methylbert / canonical_soft_labels / top156 / swn / linear_clip_normalize	methylbert|canonical_soft_labels||top156|swn|linear_clip_normalize	0.9813723366118896	0.6314337684303575	99	228	164
+dismir	hard_labels		diagbckg	nnls	vector_scaling	dismir / hard_labels / diagbckg / nnls / vector_scaling	dismir|hard_labels||diagbckg|nnls|vector_scaling	0.979627066779678	0.6565459803009593	142	188	165
+methylbert	canonical_soft_labels		diagbckg	nnls	linear_clip_normalize	methylbert / canonical_soft_labels / diagbckg / nnls / linear_clip_normalize	methylbert|canonical_soft_labels||diagbckg|nnls|linear_clip_normalize	0.984223076380641	0.5434255559166933	44	287	166
+dismir	soft_labels_with_pooling		top156	mlp	vector_scaling	dismir / soft_labels_with_pooling / top156 / mlp / vector_scaling	dismir|soft_labels_with_pooling||top156|mlp|vector_scaling	0.9822765508417632	0.6124656614938736	86	245	166
+methylbert	hard_labels		top156	nnls	linear_simplex_projection	methylbert / hard_labels / top156 / nnls / linear_simplex_projection	methylbert|hard_labels||top156|nnls|linear_simplex_projection	0.984739195046728	0.5233788471790006	39	293	166
+lookup	soft_labels_with_pooling		top156	swn	linear_clip_normalize	lookup / soft_labels_with_pooling / top156 / swn / linear_clip_normalize	lookup|soft_labels_with_pooling||top156|swn|linear_clip_normalize	0.9743648331089833	0.6775814822309097	220	112	166
+lookup	soft_labels_without_pooling		top156	psls	linear_simplex_projection	lookup / soft_labels_without_pooling / top156 / psls / linear_simplex_projection	lookup|soft_labels_without_pooling||top156|psls|linear_simplex_projection	0.9656479576434596	0.6990788204177236	276	56	166
+methylbert	canonical_soft_labels		diagbckg	swn	vector_scaling	methylbert / canonical_soft_labels / diagbckg / swn / vector_scaling	methylbert|canonical_soft_labels||diagbckg|swn|vector_scaling	0.982899943244847	0.6013484554524894	73	261	167
+dismir	canonical_soft_labels		top156	mlp	vector_scaling	dismir / canonical_soft_labels / top156 / mlp / vector_scaling	dismir|canonical_soft_labels||top156|mlp|vector_scaling	0.9773795776439388	0.6673286115065853	179	158	169
+dismir	canonical_soft_labels		top156	mlp	linear_clip_normalize	dismir / canonical_soft_labels / top156 / mlp / linear_clip_normalize	dismir|canonical_soft_labels||top156|mlp|linear_clip_normalize	0.9779678663224952	0.6629403185271456	168	171	170
+methylbert	soft_labels_without_pooling		top156	mlp	linear_simplex_projection	methylbert / soft_labels_without_pooling / top156 / mlp / linear_simplex_projection	methylbert|soft_labels_without_pooling||top156|mlp|linear_simplex_projection	0.9785283924415028	0.6572977347851529	158	183	171
+methylbert	hard_labels		diagbckg	nnls	linear_simplex_projection	methylbert / hard_labels / diagbckg / nnls / linear_simplex_projection	methylbert|hard_labels||diagbckg|nnls|linear_simplex_projection	0.985728591092212	0.4592436299080625	28	314	171
+methylbert	canonical_soft_labels		diagbckg	nnls	vector_scaling	methylbert / canonical_soft_labels / diagbckg / nnls / vector_scaling	methylbert|canonical_soft_labels||diagbckg|nnls|vector_scaling	0.983170357082346	0.5650872106731323	65	279	172
+lookup	soft_labels_without_pooling		top156	swn	linear_simplex_projection	lookup / soft_labels_without_pooling / top156 / swn / linear_simplex_projection	lookup|soft_labels_without_pooling||top156|swn|linear_simplex_projection	0.9508201515085526	0.7096787567336592	308	37	173
+methylbert	canonical_soft_labels		diagbckg	swn	linear_simplex_projection	methylbert / canonical_soft_labels / diagbckg / swn / linear_simplex_projection	methylbert|canonical_soft_labels||diagbckg|swn|linear_simplex_projection	0.9825341960408712	0.5990197952463866	83	263	173
+dismir	canonical_soft_labels		top156	xgb	linear_clip_normalize	dismir / canonical_soft_labels / top156 / xgb / linear_clip_normalize	dismir|canonical_soft_labels||top156|xgb|linear_clip_normalize	0.9757295182000468	0.6699139637102154	206	140	173
+dismir	hard_labels		diagbckg	psls	uncalibrated	dismir / hard_labels / diagbckg / psls / uncalibrated	dismir|hard_labels||diagbckg|psls|uncalibrated	0.976732527241497	0.667468835152242	193	156	175
+dismir	hard_labels		top156	psls	uncalibrated	dismir / hard_labels / top156 / psls / uncalibrated	dismir|hard_labels||top156|psls|uncalibrated	0.9767228916465478	0.6674439607398395	194	157	176
+methylbert	canonical_soft_labels		diagbckg	psls	linear_clip_normalize	methylbert / canonical_soft_labels / diagbckg / psls / linear_clip_normalize	methylbert|canonical_soft_labels||diagbckg|psls|linear_clip_normalize	0.9839653316557032	0.5024058924333387	49	304	177
+methylbert	hard_labels		diagbckg	psls	linear_simplex_projection	methylbert / hard_labels / diagbckg / psls / linear_simplex_projection	methylbert|hard_labels||diagbckg|psls|linear_simplex_projection	0.985401259527594	0.4279310086272839	31	323	177
+lookup	soft_labels_with_pooling		top156	mlp	linear_simplex_projection	lookup / soft_labels_with_pooling / top156 / mlp / linear_simplex_projection	lookup|soft_labels_with_pooling||top156|mlp|linear_simplex_projection	0.9713668429274016	0.6785901843454469	243	111	177
+dismir	canonical_soft_labels		top156	nnls	linear_clip_normalize	dismir / canonical_soft_labels / top156 / nnls / linear_clip_normalize	dismir|canonical_soft_labels||top156|nnls|linear_clip_normalize	0.9722536600317846	0.6760357087407792	239	116	178
+methylbert	canonical_soft_labels		diagbckg	swn	linear_clip_normalize	methylbert / canonical_soft_labels / diagbckg / swn / linear_clip_normalize	methylbert|canonical_soft_labels||diagbckg|swn|linear_clip_normalize	0.9821877221366552	0.5892438258350485	89	267	178
+cancerdetector		uniform_prior	top156	nnls	uncalibrated	cancerdetector / uniform_prior / top156 / nnls / uncalibrated	cancerdetector||uniform_prior|top156|nnls|uncalibrated	0.9798252230122122	0.6373157427236161	135	221	178
+methylbert	soft_labels_without_pooling		top156	psls	uncalibrated	methylbert / soft_labels_without_pooling / top156 / psls / uncalibrated	methylbert|soft_labels_without_pooling||top156|psls|uncalibrated	0.9773288150031452	0.661672971790659	182	177	180
+methylbert	soft_labels_without_pooling		top156	mlp	vector_scaling	methylbert / soft_labels_without_pooling / top156 / mlp / vector_scaling	methylbert|soft_labels_without_pooling||top156|mlp|vector_scaling	0.9783228061575516	0.6534854528440881	162	198	180
+dismir	hard_labels		top156	mlp	uncalibrated	dismir / hard_labels / top156 / mlp / uncalibrated	dismir|hard_labels||top156|mlp|uncalibrated	0.9738616719574538	0.6708877930576163	228	133	181
+methylbert	canonical_soft_labels		diagbckg	psls	vector_scaling	methylbert / canonical_soft_labels / diagbckg / psls / vector_scaling	methylbert|canonical_soft_labels||diagbckg|psls|vector_scaling	0.9829910990845389	0.5286524211610584	71	291	181
+dismir	hard_labels		diagbckg	nnls	uncalibrated	dismir / hard_labels / diagbckg / nnls / uncalibrated	dismir|hard_labels||diagbckg|nnls|uncalibrated	0.9772832387224344	0.6607214544861892	185	178	182
+dismir	soft_labels_with_pooling		top156	xgb	uncalibrated	dismir / soft_labels_with_pooling / top156 / xgb / uncalibrated	dismir|soft_labels_with_pooling||top156|xgb|uncalibrated	0.9744972616097198	0.6686765279168713	217	150	184
+Celfie					linear_clip_normalize	Celfie / linear_clip_normalize	celfie|||||linear_clip_normalize	0.974145856257712	0.6691386953960732	223	144	184
+lookup	soft_labels_without_pooling		top156	swn	linear_clip_normalize	lookup / soft_labels_without_pooling / top156 / swn / linear_clip_normalize	lookup|soft_labels_without_pooling||top156|swn|linear_clip_normalize	0.9334497380190768	0.7031398200037693	318	49	184
+methylbert	hard_labels		top156	psls	linear_clip_normalize	methylbert / hard_labels / top156 / psls / linear_clip_normalize	methylbert|hard_labels||top156|psls|linear_clip_normalize	0.9828831868677947	0.5216948380434975	74	296	185
+dismir	hard_labels		diagbckg	psls	vector_scaling	dismir / hard_labels / diagbckg / psls / vector_scaling	dismir|hard_labels||diagbckg|psls|vector_scaling	0.9770632688161672	0.6570978971863266	188	185	187
+dismir	canonical_soft_labels		diagbckg	psls	linear_clip_normalize	dismir / canonical_soft_labels / diagbckg / psls / linear_clip_normalize	dismir|canonical_soft_labels||diagbckg|psls|linear_clip_normalize	0.974443643348332	0.6677448645701006	218	155	187
+lookup	soft_labels_without_pooling		top156	xgb	vector_scaling	lookup / soft_labels_without_pooling / top156 / xgb / vector_scaling	lookup|soft_labels_without_pooling||top156|xgb|vector_scaling	0.8814512980163727	0.7128504755950935	350	24	187
+dismir	hard_labels		top156	psls	vector_scaling	dismir / hard_labels / top156 / psls / vector_scaling	dismir|hard_labels||top156|psls|vector_scaling	0.9769892011319914	0.6569162255080523	189	186	188
+dismir	soft_labels_with_pooling		top156	mlp	linear_simplex_projection	dismir / soft_labels_with_pooling / top156 / mlp / linear_simplex_projection	dismir|soft_labels_with_pooling||top156|mlp|linear_simplex_projection	0.9800247876688608	0.60825405747595	128	248	188
+methylbert	canonical_soft_labels		top156	psls	uncalibrated	methylbert / canonical_soft_labels / top156 / psls / uncalibrated	methylbert|canonical_soft_labels||top156|psls|uncalibrated	0.9806352449425548	0.6001455852152049	115	262	189
+methylbert	hard_labels		diagbckg	nnls	linear_clip_normalize	methylbert / hard_labels / diagbckg / nnls / linear_clip_normalize	methylbert|hard_labels||diagbckg|nnls|linear_clip_normalize	0.9832349444923149	0.4575889162012269	63	315	189
+dismir	canonical_soft_labels		top156	psls	linear_simplex_projection	dismir / canonical_soft_labels / top156 / psls / linear_simplex_projection	dismir|canonical_soft_labels||top156|psls|linear_simplex_projection	0.9701678430619062	0.67141744073675	249	130	190
+methylbert	soft_labels_without_pooling		top156	mlp	linear_clip_normalize	methylbert / soft_labels_without_pooling / top156 / mlp / linear_clip_normalize	methylbert|soft_labels_without_pooling||top156|mlp|linear_clip_normalize	0.9775426767887564	0.6462589820468704	175	207	191
+dismir	hard_labels		diagbckg	mlp	vector_scaling	dismir / hard_labels / diagbckg / mlp / vector_scaling	dismir|hard_labels||diagbckg|mlp|vector_scaling	0.9729894605266352	0.6686894454168397	235	149	192
+dismir	canonical_soft_labels		top156	xgb	uncalibrated	dismir / canonical_soft_labels / top156 / xgb / uncalibrated	dismir|canonical_soft_labels||top156|xgb|uncalibrated	0.971473023732558	0.669548074694182	242	142	192
+dismir	canonical_soft_labels		top156	mlp	uncalibrated	dismir / canonical_soft_labels / top156 / mlp / uncalibrated	dismir|canonical_soft_labels||top156|mlp|uncalibrated	0.9749282491426136	0.6628058993025691	215	172	194
+dismir	hard_labels		diagbckg	mlp	linear_clip_normalize	dismir / hard_labels / diagbckg / mlp / linear_clip_normalize	dismir|hard_labels||diagbckg|mlp|linear_clip_normalize	0.9720908851258636	0.6689439615491678	240	147	194
+methylbert	canonical_soft_labels		top156	swn	uncalibrated	methylbert / canonical_soft_labels / top156 / swn / uncalibrated	methylbert|canonical_soft_labels||top156|swn|uncalibrated	0.979014247182445	0.6230758030459741	153	236	195
+methylbert	canonical_soft_labels		diagbckg	nnls	uncalibrated	methylbert / canonical_soft_labels / diagbckg / nnls / uncalibrated	methylbert|canonical_soft_labels||diagbckg|nnls|uncalibrated	0.981195459624431	0.5434517364670225	105	286	196
+dismir	canonical_soft_labels		diagbckg	nnls	linear_clip_normalize	dismir / canonical_soft_labels / diagbckg / nnls / linear_clip_normalize	dismir|canonical_soft_labels||diagbckg|nnls|linear_clip_normalize	0.9761327962979498	0.6557354838222149	201	193	197
+methylbert	soft_labels_with_pooling		top156	xgb	linear_clip_normalize	methylbert / soft_labels_with_pooling / top156 / xgb / linear_clip_normalize	methylbert|soft_labels_with_pooling||top156|xgb|linear_clip_normalize	0.9769699674563964	0.646487030093476	190	206	198
+dismir	hard_labels		diagbckg	mlp	linear_simplex_projection	dismir / hard_labels / diagbckg / mlp / linear_simplex_projection	dismir|hard_labels||diagbckg|mlp|linear_simplex_projection	0.971242492662195	0.6685556958268493	245	151	198
+dismir	canonical_soft_labels		top156	nnls	uncalibrated	dismir / canonical_soft_labels / top156 / nnls / uncalibrated	dismir|canonical_soft_labels||top156|nnls|uncalibrated	0.9611461572441738	0.6794555952002354	289	107	198
+methylbert	hard_labels		top156	nnls	linear_clip_normalize	methylbert / hard_labels / top156 / nnls / linear_clip_normalize	methylbert|hard_labels||top156|nnls|linear_clip_normalize	0.981432312658505	0.5185995964334162	98	299	199
+methylbert	hard_labels		top156	nnls	vector_scaling	methylbert / hard_labels / top156 / nnls / vector_scaling	methylbert|hard_labels||top156|nnls|vector_scaling	0.980768209228968	0.5395534462191175	111	288	200
+methylbert	hard_labels		diagbckg	psls	linear_clip_normalize	methylbert / hard_labels / diagbckg / psls / linear_clip_normalize	methylbert|hard_labels||diagbckg|psls|linear_clip_normalize	0.9828712979025656	0.4251520464182817	77	325	201
+lookup	soft_labels_with_pooling		top156	psls	linear_clip_normalize	lookup / soft_labels_with_pooling / top156 / psls / linear_clip_normalize	lookup|soft_labels_with_pooling||top156|psls|linear_clip_normalize	0.975553226802052	0.6539223611261666	208	197	203
+EpiDISH					vector_scaling	EpiDISH / vector_scaling	epidish|||||vector_scaling	0.9595093966944118	0.6787757635377812	296	110	203
+cancerdetector		uniform_prior	top156	mlp	uncalibrated	cancerdetector / uniform_prior / top156 / mlp / uncalibrated	cancerdetector||uniform_prior|top156|mlp|uncalibrated	0.9757281567572678	0.6515685811640629	207	202	205
+methylbert	soft_labels_without_pooling		top156	nnls	uncalibrated	methylbert / soft_labels_without_pooling / top156 / nnls / uncalibrated	methylbert|soft_labels_without_pooling||top156|nnls|uncalibrated	0.9755052524237084	0.6533479755811302	210	199	205
+methylbert	hard_labels		diagbckg	nnls	vector_scaling	methylbert / hard_labels / diagbckg / nnls / vector_scaling	methylbert|hard_labels||diagbckg|nnls|vector_scaling	0.9813510293338744	0.4742650457173906	101	310	206
+methylbert	canonical_soft_labels		diagbckg	psls	uncalibrated	methylbert / canonical_soft_labels / diagbckg / psls / uncalibrated	methylbert|canonical_soft_labels||diagbckg|psls|uncalibrated	0.9810269133658952	0.5037049549876972	108	303	206
+methylbert	canonical_soft_labels		diagbckg	swn	uncalibrated	methylbert / canonical_soft_labels / diagbckg / swn / uncalibrated	methylbert|canonical_soft_labels||diagbckg|swn|uncalibrated	0.9796914407732816	0.5810707817071048	139	272	206
+dismir	canonical_soft_labels		diagbckg	psls	uncalibrated	dismir / canonical_soft_labels / diagbckg / psls / uncalibrated	dismir|canonical_soft_labels||diagbckg|psls|uncalibrated	0.9647175278403984	0.6728243890480486	282	129	206
+dismir	soft_labels_with_pooling		top156	mlp	linear_clip_normalize	dismir / soft_labels_with_pooling / top156 / mlp / linear_clip_normalize	dismir|soft_labels_with_pooling||top156|mlp|linear_clip_normalize	0.9784655078595684	0.6072577966806741	160	252	206
+dismir	canonical_soft_labels		diagbckg	psls	vector_scaling	dismir / canonical_soft_labels / diagbckg / psls / vector_scaling	dismir|canonical_soft_labels||diagbckg|psls|vector_scaling	0.9679460449751122	0.6680234412718142	259	153	206
+dismir	canonical_soft_labels		top156	psls	linear_clip_normalize	dismir / canonical_soft_labels / top156 / psls / linear_clip_normalize	dismir|canonical_soft_labels||top156|psls|linear_clip_normalize	0.9675409366406093	0.6685171353949805	260	152	206
+methylbert	hard_labels		top156	psls	vector_scaling	methylbert / hard_labels / top156 / psls / vector_scaling	methylbert|hard_labels||top156|psls|vector_scaling	0.9799780354727758	0.5442048442858367	129	285	207
+dismir	canonical_soft_labels		diagbckg	nnls	vector_scaling	dismir / canonical_soft_labels / diagbckg / nnls / vector_scaling	dismir|canonical_soft_labels||diagbckg|nnls|vector_scaling	0.9718809959907068	0.6620739824946703	241	175	208
+dismir	hard_labels		diagbckg	mlp	uncalibrated	dismir / hard_labels / diagbckg / mlp / uncalibrated	dismir|hard_labels||diagbckg|mlp|uncalibrated	0.9660160615029778	0.6694041758302617	273	143	208
+methylbert	canonical_soft_labels		top156	mlp	linear_simplex_projection	methylbert / canonical_soft_labels / top156 / mlp / linear_simplex_projection	methylbert|canonical_soft_labels||top156|mlp|linear_simplex_projection	0.9791606980802424	0.5876006539732055	149	268	209
+lookup	soft_labels_with_pooling		top156	swn	uncalibrated	lookup / soft_labels_with_pooling / top156 / swn / uncalibrated	lookup|soft_labels_with_pooling||top156|swn|uncalibrated	0.9671549906004054	0.6677591133852269	264	154	209
+methylbert	canonical_soft_labels		top156	nnls	uncalibrated	methylbert / canonical_soft_labels / top156 / nnls / uncalibrated	methylbert|canonical_soft_labels||top156|nnls|uncalibrated	0.9791353707027284	0.5874592244175505	150	269	210
+methylbert	canonical_soft_labels		top156	mlp	vector_scaling	methylbert / canonical_soft_labels / top156 / mlp / vector_scaling	methylbert|canonical_soft_labels||top156|mlp|vector_scaling	0.9790684039613128	0.5831054586953605	152	270	211
+lookup	soft_labels_with_pooling		top156	mlp	linear_clip_normalize	lookup / soft_labels_with_pooling / top156 / mlp / linear_clip_normalize	lookup|soft_labels_with_pooling||top156|mlp|linear_clip_normalize	0.9672530760746983	0.6671861551484414	263	159	211
+dismir	canonical_soft_labels		top156	psls	uncalibrated	dismir / canonical_soft_labels / top156 / psls / uncalibrated	dismir|canonical_soft_labels||top156|psls|uncalibrated	0.9592279961845678	0.6750678561175077	298	124	211
+methylbert	hard_labels		top156	psls	uncalibrated	methylbert / hard_labels / top156 / psls / uncalibrated	methylbert|hard_labels||top156|psls|uncalibrated	0.980365914606956	0.5052708994978555	122	302	212
+lookup	soft_labels_with_pooling		top156	nnls	linear_clip_normalize	lookup / soft_labels_with_pooling / top156 / nnls / linear_clip_normalize	lookup|soft_labels_with_pooling||top156|nnls|linear_clip_normalize	0.9740113857010362	0.6523324500623272	224	201	213
+lookup	soft_labels_without_pooling		top156	nnls	linear_clip_normalize	lookup / soft_labels_without_pooling / top156 / nnls / linear_clip_normalize	lookup|soft_labels_without_pooling||top156|nnls|linear_clip_normalize	0.941464425847984	0.6759763399489118	314	117	216
+lookup	soft_labels_without_pooling		top156	mlp	linear_simplex_projection	lookup / soft_labels_without_pooling / top156 / mlp / linear_simplex_projection	lookup|soft_labels_without_pooling||top156|mlp|linear_simplex_projection	0.9094759464566268	0.6843016324428373	334	97	216
+lookup	soft_labels_without_pooling		top156	swn	uncalibrated	lookup / soft_labels_without_pooling / top156 / swn / uncalibrated	lookup|soft_labels_without_pooling||top156|swn|uncalibrated	0.8862004219499117	0.6879874070848191	347	86	217
+dismir	canonical_soft_labels		top156	psls	vector_scaling	dismir / canonical_soft_labels / top156 / psls / vector_scaling	dismir|canonical_soft_labels||top156|psls|vector_scaling	0.9615900221179586	0.6689700435040191	288	146	217
+methylbert	hard_labels		diagbckg	nnls	uncalibrated	methylbert / hard_labels / diagbckg / nnls / uncalibrated	methylbert|hard_labels||diagbckg|nnls|uncalibrated	0.9805955797144666	0.4473814286001095	116	319	218
+methylbert	canonical_soft_labels		top156	mlp	linear_clip_normalize	methylbert / canonical_soft_labels / top156 / mlp / linear_clip_normalize	methylbert|canonical_soft_labels||top156|mlp|linear_clip_normalize	0.9784509427111636	0.578606749567086	161	274	218
+methylbert	canonical_soft_labels		diagbckg	mlp	vector_scaling	methylbert / canonical_soft_labels / diagbckg / mlp / vector_scaling	methylbert|canonical_soft_labels||diagbckg|mlp|vector_scaling	0.9781435775883812	0.5796028456518442	164	273	219
+methylbert	hard_labels		diagbckg	psls	vector_scaling	methylbert / hard_labels / diagbckg / psls / vector_scaling	methylbert|hard_labels||diagbckg|psls|vector_scaling	0.9804015785645184	0.4497016951264497	121	317	219
+methylbert	hard_labels		top156	swn	vector_scaling	methylbert / hard_labels / top156 / swn / vector_scaling	methylbert|hard_labels||top156|swn|vector_scaling	0.979478004828608	0.5203052691293485	143	298	221
+methylbert	soft_labels_without_pooling		top156	mlp	uncalibrated	methylbert / soft_labels_without_pooling / top156 / mlp / uncalibrated	methylbert|soft_labels_without_pooling||top156|mlp|uncalibrated	0.9742804344391224	0.6393882987948545	222	219	221
+methylbert	canonical_soft_labels		diagbckg	mlp	linear_clip_normalize	methylbert / canonical_soft_labels / diagbckg / mlp / linear_clip_normalize	methylbert|canonical_soft_labels||diagbckg|mlp|linear_clip_normalize	0.977811498769017	0.5765436031161861	173	275	224
+methylbert	hard_labels		diagbckg	psls	uncalibrated	methylbert / hard_labels / diagbckg / psls / uncalibrated	methylbert|hard_labels||diagbckg|psls|uncalibrated	0.9803529536955768	0.4163875943007543	123	328	226
+Houseman_ineq					vector_scaling	Houseman_ineq / vector_scaling	epidish_houseman|||||vector_scaling	0.9605289934145812	0.6659448619009677	291	161	226
+methylbert	soft_labels_without_pooling		top156	xgb	linear_simplex_projection	methylbert / soft_labels_without_pooling / top156 / xgb / linear_simplex_projection	methylbert|soft_labels_without_pooling||top156|xgb|linear_simplex_projection	0.974501224542801	0.6224762959110193	216	237	227
+dismir	canonical_soft_labels		diagbckg	nnls	uncalibrated	dismir / canonical_soft_labels / diagbckg / nnls / uncalibrated	dismir|canonical_soft_labels||diagbckg|nnls|uncalibrated	0.9658717560350494	0.6603376734959843	274	179	227
+methylbert	hard_labels		diagbckg	swn	vector_scaling	methylbert / hard_labels / diagbckg / swn / vector_scaling	methylbert|hard_labels||diagbckg|swn|vector_scaling	0.9792447883696858	0.4787047790938785	148	309	229
+methylbert	canonical_soft_labels		diagbckg	mlp	linear_simplex_projection	methylbert / canonical_soft_labels / diagbckg / mlp / linear_simplex_projection	methylbert|canonical_soft_labels||diagbckg|mlp|linear_simplex_projection	0.9772268241508306	0.5821465847489331	186	271	229
+methylbert	soft_labels_with_pooling		top156	xgb	uncalibrated	methylbert / soft_labels_with_pooling / top156 / xgb / uncalibrated	methylbert|soft_labels_with_pooling||top156|xgb|uncalibrated	0.9726862832915044	0.6326150579665577	236	225	231
+lookup	soft_labels_without_pooling		top156	mlp	linear_clip_normalize	lookup / soft_labels_without_pooling / top156 / mlp / linear_clip_normalize	lookup|soft_labels_without_pooling||top156|mlp|linear_clip_normalize	0.8901049436213858	0.6754606274078391	346	121	234
+methylbert	hard_labels		top156	swn	linear_clip_normalize	methylbert / hard_labels / top156 / swn / linear_clip_normalize	methylbert|hard_labels||top156|swn|linear_clip_normalize	0.9783084816756776	0.5018291644314944	163	305	234
+lookup	soft_labels_with_pooling		top156	mlp	uncalibrated	lookup / soft_labels_with_pooling / top156 / mlp / uncalibrated	lookup|soft_labels_with_pooling||top156|mlp|uncalibrated	0.9603634759340336	0.6619605876682914	294	176	235
+methylbert	hard_labels		top156	nnls	uncalibrated	methylbert / hard_labels / top156 / nnls / uncalibrated	methylbert|hard_labels||top156|nnls|uncalibrated	0.9780757772811238	0.4998085773693708	166	307	237
+methylbert	soft_labels_without_pooling		top156	xgb	vector_scaling	methylbert / soft_labels_without_pooling / top156 / xgb / vector_scaling	methylbert|soft_labels_without_pooling||top156|xgb|vector_scaling	0.9689747014178784	0.6395385968014501	255	218	237
+methylbert	hard_labels		top156	swn	linear_simplex_projection	methylbert / hard_labels / top156 / swn / linear_simplex_projection	methylbert|hard_labels||top156|swn|linear_simplex_projection	0.9778862644352828	0.5011529936559359	171	306	239
+lookup	soft_labels_without_pooling		top156	psls	linear_clip_normalize	lookup / soft_labels_without_pooling / top156 / psls / linear_clip_normalize	lookup|soft_labels_without_pooling||top156|psls|linear_clip_normalize	0.938128688308277	0.6658477536026853	316	162	239
+methylbert	hard_labels		diagbckg	swn	linear_simplex_projection	methylbert / hard_labels / diagbckg / swn / linear_simplex_projection	methylbert|hard_labels||diagbckg|swn|linear_simplex_projection	0.977981732422728	0.4613700785677471	167	313	240
+dismir	soft_labels_with_pooling		top156	mlp	uncalibrated	dismir / soft_labels_with_pooling / top156 / mlp / uncalibrated	dismir|soft_labels_with_pooling||top156|mlp|uncalibrated	0.9738926637300732	0.6070905904459072	227	253	240
+methylbert	canonical_soft_labels		top156	mlp	uncalibrated	methylbert / canonical_soft_labels / top156 / mlp / uncalibrated	methylbert|canonical_soft_labels||top156|mlp|uncalibrated	0.9758809406592552	0.5705586317752142	205	276	241
+methylbert	hard_labels		diagbckg	swn	linear_clip_normalize	methylbert / hard_labels / diagbckg / swn / linear_clip_normalize	methylbert|hard_labels||diagbckg|swn|linear_clip_normalize	0.9779296483279448	0.4621324812562306	170	312	241
+lookup	soft_labels_with_pooling		top156	psls	uncalibrated	lookup / soft_labels_with_pooling / top156 / psls / uncalibrated	lookup|soft_labels_with_pooling||top156|psls|uncalibrated	0.9665390741541592	0.6381380629336383	269	220	245
+UXM U25					linear_simplex_projection	UXM U25 / linear_simplex_projection	uxm|||||linear_simplex_projection	0.967366249650194	0.6265511034927271	262	233	248
+Houseman_ineq					linear_simplex_projection	Houseman_ineq / linear_simplex_projection	epidish_houseman|||||linear_simplex_projection	0.9651151181673008	0.6399096425435296	279	217	248
+UXM U25					vector_scaling	UXM U25 / vector_scaling	uxm|||||vector_scaling	0.9633194016381504	0.6417531230582848	286	214	250
+Celfie					uncalibrated	Celfie / uncalibrated	celfie|||||uncalibrated	0.9604952516010016	0.6446742406411757	292	208	250
+methylbert	canonical_soft_labels		top156	xgb	vector_scaling	methylbert / canonical_soft_labels / top156 / xgb / vector_scaling	methylbert|canonical_soft_labels||top156|xgb|vector_scaling	0.972591482655737	0.5945965443418557	237	264	251
+lookup	soft_labels_with_pooling		top156	nnls	uncalibrated	lookup / soft_labels_with_pooling / top156 / nnls / uncalibrated	lookup|soft_labels_with_pooling||top156|nnls|uncalibrated	0.9651276466783032	0.6354084604713387	278	224	251
+methylbert	soft_labels_without_pooling		top156	xgb	linear_clip_normalize	methylbert / soft_labels_without_pooling / top156 / xgb / linear_clip_normalize	methylbert|soft_labels_without_pooling||top156|xgb|linear_clip_normalize	0.9685390083855158	0.607691531549722	257	250	254
+dismir	canonical_soft_labels		diagbckg	mlp	linear_simplex_projection	dismir / canonical_soft_labels / diagbckg / mlp / linear_simplex_projection	dismir|canonical_soft_labels||diagbckg|mlp|linear_simplex_projection	0.9666395546947624	0.6151875068741255	268	240	254
+methylbert	canonical_soft_labels		diagbckg	mlp	uncalibrated	methylbert / canonical_soft_labels / diagbckg / mlp / uncalibrated	methylbert|canonical_soft_labels||diagbckg|mlp|uncalibrated	0.9729967087335532	0.5700365980151525	234	277	256
+dismir	canonical_soft_labels		diagbckg	mlp	vector_scaling	dismir / canonical_soft_labels / diagbckg / mlp / vector_scaling	dismir|canonical_soft_labels||diagbckg|mlp|vector_scaling	0.9664600836475592	0.6145438520406379	270	242	256
+dismir	canonical_soft_labels		diagbckg	mlp	linear_clip_normalize	dismir / canonical_soft_labels / diagbckg / mlp / linear_clip_normalize	dismir|canonical_soft_labels||diagbckg|mlp|linear_clip_normalize	0.9663571243950934	0.6146119057585812	271	241	256
+methylbert	hard_labels		top156	swn	uncalibrated	methylbert / hard_labels / top156 / swn / uncalibrated	methylbert|hard_labels||top156|swn|uncalibrated	0.9755312772385376	0.4814953218227876	209	308	259
+dismir	canonical_soft_labels		top156	swn	uncalibrated	dismir / canonical_soft_labels / top156 / swn / uncalibrated	dismir|canonical_soft_labels||top156|swn|uncalibrated	0.9652453358254092	0.6080887649293569	277	249	263
+methylbert	canonical_soft_labels		top156	xgb	linear_simplex_projection	methylbert / canonical_soft_labels / top156 / xgb / linear_simplex_projection	methylbert|canonical_soft_labels||top156|xgb|linear_simplex_projection	0.9703886935156716	0.5642672622734144	248	280	264
+lookup	soft_labels_without_pooling		top156	mlp	uncalibrated	lookup / soft_labels_without_pooling / top156 / mlp / uncalibrated	lookup|soft_labels_without_pooling||top156|mlp|uncalibrated	0.8409997371157384	0.6641894945255695	361	167	264
+dismir	canonical_soft_labels		top156	swn	linear_clip_normalize	dismir / canonical_soft_labels / top156 / swn / linear_clip_normalize	dismir|canonical_soft_labels||top156|swn|linear_clip_normalize	0.96577874946369	0.6066642603723118	275	255	265
+dismir	canonical_soft_labels		diagbckg	mlp	uncalibrated	dismir / canonical_soft_labels / diagbckg / mlp / uncalibrated	dismir|canonical_soft_labels||diagbckg|mlp|uncalibrated	0.9621696454389692	0.6139561667113757	287	243	265
+methylbert	hard_labels		top156	mlp	linear_simplex_projection	methylbert / hard_labels / top156 / mlp / linear_simplex_projection	methylbert|hard_labels||top156|mlp|linear_simplex_projection	0.9699969771540696	0.5544184801574378	250	281	266
+methylbert	hard_labels		top156	mlp	vector_scaling	methylbert / hard_labels / top156 / mlp / vector_scaling	methylbert|hard_labels||top156|mlp|vector_scaling	0.9698828525476826	0.5652314493476237	253	278	266
+EpiDISH					linear_simplex_projection	EpiDISH / linear_simplex_projection	epidish|||||linear_simplex_projection	0.9590153577112728	0.6269257752759789	300	232	266
+lookup	soft_labels_with_pooling		top156	xgb	vector_scaling	lookup / soft_labels_with_pooling / top156 / xgb / vector_scaling	lookup|soft_labels_with_pooling||top156|xgb|vector_scaling	0.932182422522156	0.6433044434873	320	212	266
+methylbert	hard_labels		top156	mlp	linear_clip_normalize	methylbert / hard_labels / top156 / mlp / linear_clip_normalize	methylbert|hard_labels||top156|mlp|linear_clip_normalize	0.9699546224660262	0.546093409721603	251	284	268
+methylbert	hard_labels		diagbckg	mlp	linear_clip_normalize	methylbert / hard_labels / diagbckg / mlp / linear_clip_normalize	methylbert|hard_labels||diagbckg|mlp|linear_clip_normalize	0.9743337537554744	0.450403168080724	221	316	269
+methylbert	hard_labels		diagbckg	mlp	vector_scaling	methylbert / hard_labels / diagbckg / mlp / vector_scaling	methylbert|hard_labels||diagbckg|mlp|vector_scaling	0.9739292093097068	0.4652141601552911	226	311	269
+Houseman_ineq					linear_clip_normalize	Houseman_ineq / linear_clip_normalize	epidish_houseman|||||linear_clip_normalize	0.9417614896048736	0.6321711977115294	313	226	270
+dismir	canonical_soft_labels		top156	swn	linear_simplex_projection	dismir / canonical_soft_labels / top156 / swn / linear_simplex_projection	dismir|canonical_soft_labels||top156|swn|linear_simplex_projection	0.9640203230325212	0.6054279514695621	283	257	270
+methylbert	hard_labels		diagbckg	swn	uncalibrated	methylbert / hard_labels / diagbckg / swn / uncalibrated	methylbert|hard_labels||diagbckg|swn|uncalibrated	0.974431385821913	0.4449634686013121	219	322	271
+UXM U25					linear_clip_normalize	UXM U25 / linear_clip_normalize	uxm|||||linear_clip_normalize	0.9590530619146902	0.6116008213750364	299	246	273
+lookup	soft_labels_with_pooling		top156	xgb	linear_simplex_projection	lookup / soft_labels_with_pooling / top156 / xgb / linear_simplex_projection	lookup|soft_labels_with_pooling||top156|xgb|linear_simplex_projection	0.9331307497608928	0.6314427305638436	319	227	273
+methylbert	hard_labels		diagbckg	mlp	linear_simplex_projection	methylbert / hard_labels / diagbckg / mlp / linear_simplex_projection	methylbert|hard_labels||diagbckg|mlp|linear_simplex_projection	0.9736184238960672	0.4488562869686171	230	318	274
+dismir	canonical_soft_labels		top156	swn	vector_scaling	dismir / canonical_soft_labels / top156 / swn / vector_scaling	dismir|canonical_soft_labels||top156|swn|vector_scaling	0.957874685396496	0.6074393681117423	301	251	276
+methylbert	soft_labels_without_pooling		top156	xgb	uncalibrated	methylbert / soft_labels_without_pooling / top156 / xgb / uncalibrated	methylbert|soft_labels_without_pooling||top156|xgb|uncalibrated	0.9597957324967314	0.6018319412308407	295	260	278
+lookup	soft_labels_without_pooling		top156	xgb	linear_simplex_projection	lookup / soft_labels_without_pooling / top156 / xgb / linear_simplex_projection	lookup|soft_labels_without_pooling||top156|xgb|linear_simplex_projection	0.8796663231699264	0.6484911315851813	352	203	278
+methylbert	hard_labels		top156	mlp	uncalibrated	methylbert / hard_labels / top156 / mlp / uncalibrated	methylbert|hard_labels||top156|mlp|uncalibrated	0.9668630626011409	0.5308943481890962	266	290	278
+lookup	soft_labels_with_pooling		top156	xgb	linear_clip_normalize	lookup / soft_labels_with_pooling / top156 / xgb / linear_clip_normalize	lookup|soft_labels_with_pooling||top156|xgb|linear_clip_normalize	0.9246752057855526	0.6157992669133736	323	239	281
+Houseman_ineq					uncalibrated	Houseman_ineq / uncalibrated	epidish_houseman|||||uncalibrated	0.9094137280725918	0.6304023096670268	335	229	282
+methylbert	canonical_soft_labels		top156	xgb	linear_clip_normalize	methylbert / canonical_soft_labels / top156 / xgb / linear_clip_normalize	methylbert|canonical_soft_labels||top156|xgb|linear_clip_normalize	0.9637393676071628	0.5516187318892062	284	282	283
+UXM U25					uncalibrated	UXM U25 / uncalibrated	uxm|||||uncalibrated	0.9485689943195332	0.6027893917256765	310	259	285
+lookup	soft_labels_with_pooling		top156	xgb	uncalibrated	lookup / soft_labels_with_pooling / top156 / xgb / uncalibrated	lookup|soft_labels_with_pooling||top156|xgb|uncalibrated	0.9065176417458268	0.6271225268187544	338	231	285
+EpiDISH					linear_clip_normalize	EpiDISH / linear_clip_normalize	epidish|||||linear_clip_normalize	0.9352763357183804	0.6067134594072809	317	254	286
+methylbert	hard_labels		diagbckg	xgb	vector_scaling	methylbert / hard_labels / diagbckg / xgb / vector_scaling	methylbert|hard_labels||diagbckg|xgb|vector_scaling	0.9712825750616068	0.4101924907702685	244	329	287
+lookup	soft_labels_without_pooling		top156	nnls	uncalibrated	lookup / soft_labels_without_pooling / top156 / nnls / uncalibrated	lookup|soft_labels_without_pooling||top156|nnls|uncalibrated	0.8750529039775802	0.6372055863646	354	222	288
+methylbert	hard_labels		diagbckg	mlp	uncalibrated	methylbert / hard_labels / diagbckg / mlp / uncalibrated	methylbert|hard_labels||diagbckg|mlp|uncalibrated	0.968574464223038	0.4277754971693899	256	324	290
+methylbert	canonical_soft_labels		diagbckg	xgb	vector_scaling	methylbert / canonical_soft_labels / diagbckg / xgb / vector_scaling	methylbert|canonical_soft_labels||diagbckg|xgb|vector_scaling	0.9708792505796828	0.3999214033112846	246	336	291
+lookup	soft_labels_without_pooling		top156	psls	uncalibrated	lookup / soft_labels_without_pooling / top156 / psls / uncalibrated	lookup|soft_labels_without_pooling||top156|psls|uncalibrated	0.8686408689093172	0.6250425270949951	355	235	295
+methylbert	canonical_soft_labels		top156	xgb	uncalibrated	methylbert / canonical_soft_labels / top156 / xgb / uncalibrated	methylbert|canonical_soft_labels||top156|xgb|uncalibrated	0.9570957295949182	0.5320471212005247	302	289	296
+methylbert	canonical_soft_labels		diagbckg	xgb	linear_simplex_projection	methylbert / canonical_soft_labels / diagbckg / xgb / linear_simplex_projection	methylbert|canonical_soft_labels||diagbckg|xgb|linear_simplex_projection	0.9675291793775014	0.3910486322695971	261	337	299
+methylbert	hard_labels		diagbckg	xgb	linear_simplex_projection	methylbert / hard_labels / diagbckg / xgb / linear_simplex_projection	methylbert|hard_labels||diagbckg|xgb|linear_simplex_projection	0.9670126880268468	0.3891316931191492	265	338	302
+lookup	soft_labels_without_pooling		top156	xgb	linear_clip_normalize	lookup / soft_labels_without_pooling / top156 / xgb / linear_clip_normalize	lookup|soft_labels_without_pooling||top156|xgb|linear_clip_normalize	0.8534603757033044	0.6131810606586569	360	244	302
+dismir	canonical_soft_labels		diagbckg	swn	uncalibrated	dismir / canonical_soft_labels / diagbckg / swn / uncalibrated	dismir|canonical_soft_labels||diagbckg|swn|uncalibrated	0.9480508723394572	0.5229086376321633	311	294	303
+EpiDISH					uncalibrated	EpiDISH / uncalibrated	epidish|||||uncalibrated	0.8941107302433711	0.5918250876959514	339	266	303
+dismir	canonical_soft_labels		diagbckg	swn	linear_clip_normalize	dismir / canonical_soft_labels / diagbckg / swn / linear_clip_normalize	dismir|canonical_soft_labels||diagbckg|swn|linear_clip_normalize	0.9458205870149896	0.5217287694369206	312	295	304
+dismir	canonical_soft_labels		diagbckg	swn	linear_simplex_projection	dismir / canonical_soft_labels / diagbckg / swn / linear_simplex_projection	dismir|canonical_soft_labels||diagbckg|swn|linear_simplex_projection	0.9399810894591596	0.5212060992138198	315	297	306
+dismir	canonical_soft_labels		diagbckg	swn	vector_scaling	dismir / canonical_soft_labels / diagbckg / swn / vector_scaling	dismir|canonical_soft_labels||diagbckg|swn|vector_scaling	0.9310125210617738	0.5181306009983713	321	300	311
+methylbert	canonical_soft_labels		diagbckg	xgb	linear_clip_normalize	methylbert / canonical_soft_labels / diagbckg / xgb / linear_clip_normalize	methylbert|canonical_soft_labels||diagbckg|xgb|linear_clip_normalize	0.9606797422256704	0.3864512762764024	290	340	315
+lookup	soft_labels_without_pooling		top156	xgb	uncalibrated	lookup / soft_labels_without_pooling / top156 / xgb / uncalibrated	lookup|soft_labels_without_pooling||top156|xgb|uncalibrated	0.7678299299431279	0.6058001510281782	374	256	315
+methylbert	hard_labels		diagbckg	xgb	linear_clip_normalize	methylbert / hard_labels / diagbckg / xgb / linear_clip_normalize	methylbert|hard_labels||diagbckg|xgb|linear_clip_normalize	0.9604738854238608	0.3877439886763038	293	339	316
+methylbert	canonical_soft_labels		diagbckg	xgb	uncalibrated	methylbert / canonical_soft_labels / diagbckg / xgb / uncalibrated	methylbert|canonical_soft_labels||diagbckg|xgb|uncalibrated	0.9536876293599952	0.371974133574143	304	341	323
+methylbert	hard_labels		top156	xgb	linear_simplex_projection	methylbert / hard_labels / top156 / xgb / linear_simplex_projection	methylbert|hard_labels||top156|xgb|linear_simplex_projection	0.9650535763217486	0.2597621935824358	281	366	324
+methylbert	hard_labels		top156	xgb	vector_scaling	methylbert / hard_labels / top156 / xgb / vector_scaling	methylbert|hard_labels||top156|xgb|vector_scaling	0.9651140342271116	0.2513426140726231	280	369	325
+methylbert	hard_labels		diagbckg	xgb	uncalibrated	methylbert / hard_labels / diagbckg / xgb / uncalibrated	methylbert|hard_labels||diagbckg|xgb|uncalibrated	0.9526444092266148	0.3693956272872996	305	344	325
+lookup	hard_labels		diagbckg	xgb	vector_scaling	lookup / hard_labels / diagbckg / xgb / vector_scaling	lookup|hard_labels||diagbckg|xgb|vector_scaling	0.8925124059755473	0.4469939461691248	343	321	332
+lookup	hard_labels		top156	xgb	vector_scaling	lookup / hard_labels / top156 / xgb / vector_scaling	lookup|hard_labels||top156|xgb|vector_scaling	0.8925124059755473	0.4469939461691248	343	321	332
+methylbert	hard_labels		top156	xgb	linear_clip_normalize	methylbert / hard_labels / top156 / xgb / linear_clip_normalize	methylbert|hard_labels||top156|xgb|linear_clip_normalize	0.959451287891978	0.2595239876586996	297	367	332
+lookup	hard_labels		top156	nnls	vector_scaling	lookup / hard_labels / top156 / nnls / vector_scaling	lookup|hard_labels||top156|nnls|vector_scaling	0.909666141585115	0.3701972573215738	332	343	337
+methylbert	hard_labels		top156	xgb	uncalibrated	methylbert / hard_labels / top156 / xgb / uncalibrated	methylbert|hard_labels||top156|xgb|uncalibrated	0.9509967595028496	0.2564788166288729	307	368	338
+lookup	hard_labels		diagbckg	nnls	vector_scaling	lookup / hard_labels / diagbckg / nnls / vector_scaling	lookup|hard_labels||diagbckg|nnls|vector_scaling	0.9096661415851148	0.3701972573215738	333	343	338
+lookup	hard_labels		diagbckg	psls	vector_scaling	lookup / hard_labels / diagbckg / psls / vector_scaling	lookup|hard_labels||diagbckg|psls|vector_scaling	0.8802284387166424	0.4208791496810208	351	327	339
+lookup	hard_labels		top156	xgb	linear_simplex_projection	lookup / hard_labels / top156 / xgb / linear_simplex_projection	lookup|hard_labels||top156|xgb|linear_simplex_projection	0.8846094574455851	0.4056290229272045	349	331	340
+lookup	hard_labels		diagbckg	xgb	linear_simplex_projection	lookup / hard_labels / diagbckg / xgb / linear_simplex_projection	lookup|hard_labels||diagbckg|xgb|linear_simplex_projection	0.8846094574455851	0.4056290229272045	349	331	340
+lookup	hard_labels		top156	psls	vector_scaling	lookup / hard_labels / top156 / psls / vector_scaling	lookup|hard_labels||top156|psls|vector_scaling	0.8783830690358302	0.4212292755807151	353	326	340
+lookup	hard_labels		diagbckg	swn	linear_simplex_projection	lookup / hard_labels / diagbckg / swn / linear_simplex_projection	lookup|hard_labels||diagbckg|swn|linear_simplex_projection	0.9152336786096188	0.3297576482116188	325	355	340
+lookup	hard_labels		top156	nnls	linear_simplex_projection	lookup / hard_labels / top156 / nnls / linear_simplex_projection	lookup|hard_labels||top156|nnls|linear_simplex_projection	0.9137927709408136	0.3393738347729674	329	352	340
+lookup	hard_labels		diagbckg	nnls	linear_simplex_projection	lookup / hard_labels / diagbckg / nnls / linear_simplex_projection	lookup|hard_labels||diagbckg|nnls|linear_simplex_projection	0.9137927709408136	0.3393738347729674	329	352	340
+lookup	hard_labels		top156	psls	linear_simplex_projection	lookup / hard_labels / top156 / psls / linear_simplex_projection	lookup|hard_labels||top156|psls|linear_simplex_projection	0.9140081859475322	0.3297150848849868	326	357	341
+lookup	hard_labels		diagbckg	psls	linear_simplex_projection	lookup / hard_labels / diagbckg / psls / linear_simplex_projection	lookup|hard_labels||diagbckg|psls|linear_simplex_projection	0.9140081760528534	0.3297150848849868	327	357	342
+lookup	hard_labels		top156	swn	linear_simplex_projection	lookup / hard_labels / top156 / swn / linear_simplex_projection	lookup|hard_labels||top156|swn|linear_simplex_projection	0.91556275921001	0.309568328492628	324	363	344
+lookup	hard_labels		diagbckg	xgb	linear_clip_normalize	lookup / hard_labels / diagbckg / xgb / linear_clip_normalize	lookup|hard_labels||diagbckg|xgb|linear_clip_normalize	0.8676419140099911	0.4025270496942212	357	333	345
+lookup	hard_labels		top156	xgb	linear_clip_normalize	lookup / hard_labels / top156 / xgb / linear_clip_normalize	lookup|hard_labels||top156|xgb|linear_clip_normalize	0.8676419140099911	0.4025270496942212	357	333	345
+lookup	hard_labels		diagbckg	swn	linear_clip_normalize	lookup / hard_labels / diagbckg / swn / linear_clip_normalize	lookup|hard_labels||diagbckg|swn|linear_clip_normalize	0.9118694514518793	0.3271535990811949	331	359	345
+lookup	hard_labels		top156	swn	linear_clip_normalize	lookup / hard_labels / top156 / swn / linear_clip_normalize	lookup|hard_labels||top156|swn|linear_clip_normalize	0.9121163957897336	0.3090753371026266	330	364	347
+lookup	hard_labels		diagbckg	swn	vector_scaling	lookup / hard_labels / diagbckg / swn / vector_scaling	lookup|hard_labels||diagbckg|swn|vector_scaling	0.90864603266789	0.329228439600143	337	358	348
+lookup	hard_labels		top156	swn	vector_scaling	lookup / hard_labels / top156 / swn / vector_scaling	lookup|hard_labels||top156|swn|vector_scaling	0.9090733998631464	0.3210782522193287	336	362	349
+lookup	hard_labels		diagbckg	nnls	linear_clip_normalize	lookup / hard_labels / diagbckg / nnls / linear_clip_normalize	lookup|hard_labels||diagbckg|nnls|linear_clip_normalize	0.8923638461102139	0.3302310563286897	345	354	349
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