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kreview
kreview에는 msk-access에서 수집한 skills 10개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.
이 저장소의 skills
Testing conventions, patterns, and best practices for kreview. MANDATORY reading before adding or modifying tests.
nbdev cell directives, module export conventions, editing workflow, and testing patterns for notebook-first development. MANDATORY reading before editing any kreview Python module.
5-tier ctDNA labeling hierarchy with IMPACT tissue rescue, CH hotspot filtering, configurable VAF/variant thresholds, and continuous VAF regression stats.
How to parse cBioPortal MAF, SV, CNA, and clinical files for the labeling pipeline.
Structured debugging framework for kreview data pipeline issues
DuckDB glob scan patterns, connection management, and performance for kreview
cfDNA fragmentomics biology — what each feature measures and how to interpret it for ctDNA detection.
Git workflow conventions, conventional commits, and PR patterns for kreview
How to load krewlyzer parquet feature files with correct suffixes and schemas per feature type.
scikit-learn patterns for single-feature LR and RF evaluation with cross-validation and AUC scoring.