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autoencodix
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BBOmix

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Tabular Regression
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100K<n<1M
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hyperparameter-optimization
automl
bioinformatics
multi-omics
autoencoder
benchmark
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metadata 
license: apache-2.0
task_categories:
  - tabular-regression
tags:
  - hyperparameter-optimization
  - automl
  - bioinformatics
  - multi-omics
  - autoencoder
  - benchmark
size_categories:
  - 100K<n<1M

BBOmix: Raw HPO Data

BBOmix is a large-scale tabular benchmark for hyperparameter optimization (HPO) of unsupervised autoencoder (AE) representation learning on real-world biological multi-omics data. This dataset contains the raw, per-run evaluation records underlying the benchmark: 105,000 individual training runs spanning 5 AE architectures, 7 dataset-modality tasks, 1,000 sampled hyperparameter configurations per architecture, and 3 random seeds.

This collection is the unprocessed source data. It was used to build the serialized tabular blackboxes used in the BBOmix paper. If you want to run HPO optimizers against the benchmark directly, use Syne Tune. Use this raw data if you want to build your own surrogate/analysis pipeline.

Total size: ~1 GB (uncompressed JSON) Total files: 105,000 JSON files (one per training run)

The files are now stored as 4 separate ZIP archives based on their architecture: vanillix.zip, varix.zip, disentanglix.zip, and ontix.zip.

Downloading and using the data

The raw JSON files are packaged into four zip archives based on their architecture, hosted on Hugging Face at autoencodix/BBOmix:

  • vanillix.zip
  • varix.zip
  • disentanglix.zip
  • ontix.zip

You can download them using the huggingface_hub Python library. For example, to download and extract the varix data:

from huggingface_hub import hf_hub_download
import zipfile

# Download the zip file
file_path = hf_hub_download(repo_id="autoencodix/BBOmix", filename="varix.zip", repo_type="dataset")

# Extract to your desired directory
with zipfile.ZipFile(file_path, 'r') as zip_ref:
    zip_ref.extractall("bbomix_raw/")

Alternatively, you can use the Hugging Face CLI:

huggingface-cli download autoencodix/BBOmix varix.zip --repo-type dataset
unzip varix.zip -d raw_hpo_data/

Once extracted, you will have access to the individual JSON files, which can be loaded and parsed using standard tools (e.g., Python's json or pandas libraries).

What's in the benchmark

  • 5 AE architectures: Vanillix (standard AE), Varix (β-VAE), Disentanglix (β-TCVAE-style disentangled VAE), and two Ontix variants (ontology-constrained VAE, evaluated with two ontology variants: chromosome and reactome)
  • 2 source datasets: TCGA (The Cancer Genome Atlas, bulk multi-omics, pan-cancer) and SCHC (Single-Cell Human Cortex, single-cell multi-omics)
  • 7 dataset-modality tasks: three TCGA modalities (RNA, EPIGENETIC, DNA) and their combinations, plus two SCHC modalities (RNA, EPIGENETIC) and their combination — each task always includes the relevant clinical/annotation labels (CLIN) used for downstream evaluation
  • 1,000 hyperparameter configurations sampled uniformly at random per architecture
  • 3 random seeds per configuration to account for stochastic training variation
  • 300 training epochs per run, with reconstruction loss logged at every epoch to support multi-fidelity / early-stopping HPO methods

Each configuration was trained on real biological data and evaluated both by its unsupervised reconstruction loss and by downstream supervised task performance (logistic regression AUC-ROC on frozen embeddings), enabling analysis of how well reconstruction loss serves as a cheap proxy for downstream utility — see the paper for details.

Directory / file naming

Each JSON file corresponds to a single training run. Files are named/grouped by architecture, dataset, modality combination, seed, and hyperparameter configuration index, following the RUN_ID field inside the file itself, e.g.:

disentanglix_schc_1_METH_CLIN_hp0.json

reads as: architecture = disentanglix, dataset = schc, seed = 1, modalities = METH + CLIN, hyperparameter configuration index = hp0.

For Ontix runs, the ontology variant (chromosome or reactome) is also encoded in the filename/RUN_ID, since the two ontologies define distinct decoder structures and are treated as separate architecture variants.

JSON schema

Every file has the same top-level schema:

Field Type Description
RUN_ID string Unique identifier for this run (see naming convention above).
ARCHITECTURE string One of vanillix, varix, disentanglix, ontix.
SEED int Random seed used for this run (0, 1, or 2).
DATASET string Source dataset: tcga or schc.
MODALITIES list[string] Data modalities used as input, e.g. ["METH", "CLIN"]. CLIN denotes the clinical/annotation labels used for downstream evaluation, not a training input modality.
ONTOLOGY string or null For Ontix runs: "chromosome" or "reactome". null for all other architectures.
HYPERPARAMETERS dict The sampled hyperparameter configuration for this run. Keys vary by architecture — see Hyperparameter search spaces below.
AVG_ML_TASK_PERFORMANCE float Downstream performance (AUC-ROC), averaged across all downstream tasks defined for this dataset/modality combination. This is the paper's primary "downstream utility" metric.
PER_TASK_PERFORMANCE dict Downstream AUC-ROC broken down per individual clinical/annotation task (e.g. sex, age_group, cancer subtype, survival status — task names depend on DATASET; see Appendix A of the paper for the full list per dataset).
VALID_RECON_LOSS float Final validation-set reconstruction loss (or ELBO/total loss for VAE-style architectures) at the last logged epoch.
loss_per_epoch dict[str, float] Per-epoch training/validation loss curve, keyed by epoch index ("0" through "299") as strings. Enables multi-fidelity and early-stopping HPO methods (e.g. ASHA, BOHB, Hyperband) to query intermediate fidelities without retraining.
RUNTIME_SECONDS float Wall-clock training time for this run in seconds.

Hyperparameter search spaces

The keys present in HYPERPARAMETERS depend on ARCHITECTURE:

  • Shared across all architectures: k_filter (input filter/feature size), n_layers (number of encoder layers), enc_factor (encoding factor), learning_rate, batch_size, drop_p (dropout probability), weight_decay, epochs, checkpoint_interval, loss_reduction
  • Vanillix: shared parameters only, plus latent_dim
  • Varix / Ontix: adds beta (KL-divergence weight)
  • Disentanglix: adds beta_mi (mutual information weight), beta_tc (total-correlation weight), beta_dimKL (per-dimension KL weight)
  • Ontix: latent_dim is architecture-defined by the chosen ontology rather than sampled (see ONTOLOGY field)

See Table 1 in the paper for the exact sampling distributions (uniform vs. log-uniform vs. categorical) for every hyperparameter.

Example record 

{
  "RUN_ID": "disentanglix_schc_1_METH_CLIN_hp0",
  "ARCHITECTURE": "disentanglix",
  "SEED": 1,
  "DATASET": "schc",
  "MODALITIES": ["METH", "CLIN"],
  "ONTOLOGY": null,
  "HYPERPARAMETERS": {
    "epochs": 300,
    "checkpoint_interval": 300,
    "loss_reduction": "sum",
    "k_filter": 4096,
    "n_layers": 2,
    "enc_factor": 1,
    "learning_rate": 0.009251283695699343,
    "weight_decay": 9.54041826560807e-05,
    "batch_size": 64,
    "drop_p": 0.6628240927476112,
    "beta_mi": 0.0509093966079994,
    "beta_tc": 288.99999718361437,
    "beta_dimKL": 0.00022271800642020435,
    "latent_dim": 16
  },
  "AVG_ML_TASK_PERFORMANCE": 0.6147933697669551,
  "PER_TASK_PERFORMANCE": {
    "author_cell_type": 0.7042096421209849,
    "age_group": 0.5984282088126611,
    "sex": 0.5417422583672192
  },
  "VALID_RECON_LOSS": 92.06772205753184,
  "loss_per_epoch": {
    "0": 139.4601534783552,
    "1": 132.60988325103102,
    "...": "...",
    "299": 92.06772205753184
  },
  "RUNTIME_SECONDS": 1571.6251
}

Intended use

This raw data is intended for:

  • Building custom tabular/surrogate HPO benchmarks beyond the provided Syne Tune blackboxes (e.g., different aggregation of seeds, custom multi-fidelity schedules, alternative surrogates for off-grid queries)
  • Studying the relationship between reconstruction loss and downstream biological utility across architectures and modalities
  • Hyperparameter importance analysis (e.g., permutation importance, fANOVA) using the full configuration-to-performance mapping
  • Meta-learning / transfer-learning research across tasks, architectures, and modalities, using the full per-run records rather than aggregated summaries

This dataset does not contain raw omics measurements (e.g. sequencing reads, methylation beta values, mutation calls) — only the evaluation results of models trained on that data. The underlying TCGA and SCHC data are public and de-identified; see the paper for their original sources.

Citation

If you use this dataset, please cite the BBOmix paper:

@misc{thalebombien2026bbomixtabularbenchmarkhyperparameter,
      title={BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning}, 
      author={Luca Thale-Bombien and Jan Ewald and Ralf König and Aaron Klein},
      year={2026},
      eprint={2606.05139},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2606.05139}, 
}

License

Released under the Apache 2.0 license.