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
| """SV-SPR: Short-read SV confidence model (caller-agnostic post-VCF rescoring). | |
| Quick start | |
| ----------- | |
| >>> from svspr import score, classify, SVSPR | |
| >>> | |
| >>> # Single SV | |
| >>> classify(chrom='chr1', pos=12345, end=13345, | |
| ... svtype='DEL', svlen=1000, total_alt_support=15) | |
| {'CS': 0.87, 'tier': 'high'} | |
| >>> | |
| >>> # Full VCF | |
| >>> df = score(vcf_path='input.vcf', ref_path='GRCh38.fa') | |
| >>> df[['chrom', 'pos', 'svtype', 'CS', 'tier']].head() | |
| """ | |
| from .model import SVSPR, score, classify, load_default | |
| from .features import SVCall, FEATURE_COLS, extract_one, extract_batch, from_vcf | |
| __version__ = '0.1.0' | |
| __all__ = ['SVSPR', 'score', 'classify', 'load_default', | |
| 'SVCall', 'FEATURE_COLS', 'extract_one', 'extract_batch', 'from_vcf'] | |