Tabular Classification
Scikit-learn
Joblib
genomics
structural-variants
short-tandem-repeats
variant-calling
confidence-calibration
random-forest
Instructions to use khyeom/SVSTR-Score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use khyeom/SVSTR-Score with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("khyeom/SVSTR-Score", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 1,177 Bytes
288cb91 3c7d0d1 288cb91 3c7d0d1 288cb91 3c7d0d1 288cb91 3c7d0d1 288cb91 3c7d0d1 dfbe7f7 3c7d0d1 288cb91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"release_version": "1.0",
"model_file": "str_model.joblib",
"calibrator_file": "str_calibrator.joblib",
"calibration": "isotonic regression on out-of-fold scores",
"features": [
"is_pass",
"motif_len",
"ref_copynum",
"gt_repcn_max",
"gt_repcn_min",
"expansion_over_ref",
"repci_width_max",
"spanning_reads",
"flanking_reads",
"inrepeat_reads",
"locus_depth",
"gt_hom",
"ref_tract_bp",
"spanning_frac",
"allele_vs_readlen",
"motif_is_homopolymer",
"gc_flank",
"entropy_flank",
"in_segdup",
"in_difficult",
"flank_lowmap"
],
"n_features": 21,
"missing_sentinel": -99999.0,
"tiers": {
"HIGH": "CS>=0.70",
"MODERATE": "0.50<=CS<0.70",
"WARNING": "0.30<=CS<0.50",
"LOW": "CS<0.30"
},
"tier_edges": [
0.3,
0.5,
0.7
],
"score": "CS = isotonic-calibrated P(call concordant with long-read truth)",
"variant_class": "STR",
"sklearn_version_trained": "1.7.1",
"training": {
"cohort": "HPRC",
"n_samples": 208,
"n_train_rows": 22651133,
"cv": "5-fold GroupKFold by sample",
"oof_auroc": 0.8342,
"oof_auprc": 0.886
}
} |