Data & ReproBioinformatics & GenomicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-research-tools-biomarker-signature-studio

Maintainer FreedomIntelligence · Last updated April 1, 2026

Build, validate, and visualize multi-omic biomarker signatures.

OpenClawNanoClawAnalysisReproductionbio-research-tools-biomarker-signature-studio🧠 bioos extended suitebioos extended bioinformatics suitebuild

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-research-tools-biomarker-signature-studio

Skill Snapshot

2 min

Design validated biomarker panels that are explainable, stable, and ready for translational follow-up. This skill stitches together the existing biomarker pipeline tooling, adds configurable feature-selection ensembles, a small survival-analysis hook, and artifact export so downstream lab teams can review QC outputs.
1. QC + Harmonization: Align expression matrices (samples x features) with metadata, check label balance, and compute summary stats. 2. Feature Selection Ensemble: Supports Boruta, elastic-net stability, mutual-information top-K, and mRMR with optional intersection voting. 3. Model Factory: Trains multiple estimators (Logistic L1, RandomForest, XGBoost if present) under nested CV, picks champion by AUC. 4. Explainability + Export: Produces SHAP tables/plots when packages are available, exports feature rankings and model weights. 5. Survival Hook: If metadata contains time_to_event and event the skill computes concordance for selected features via Cox model.
All logic lives in scripts/biomarker_signature_studio.py.

Source Doc

Expression matrix (--expression): CSV/TSV genes x samples or samples x genes (auto-detected by metadata match).
Metadata (--metadata): Must contain --label-column. Optional --id-column (default sample_id), time_to_event, event.
Optional gene list for filtering (--feature-list).
Output directory (--output-dir), created if missing.

Key flags:

Flag	Description
`--selectors`	Comma list of selection strategies (`boruta`, `lasso`, `mrmr`, `mi_topk`).
`--models`	Models to evaluate (`logit`, `rf`, `xgb`).
`--k-features`	Target number of features for `mrmr`/`mi_topk`.
`--survival`	Enable Cox evaluation when survival columns exist.
`--random-state`	Reproducibility.
`--nested-folds`	Outer CV folds (default 5).

Load + align inputs, infer orientation, impute missing values.
Standardize features (fit on train set only).
Run requested selectors; create intersection + union candidate lists.
For each selector output run nested CV training across requested models.
Export champion metrics (metrics.json), feature table (selected_features.csv), SHAP summary (shap_summary.csv when available), and survival stats (survival.json).

Use bio-research-tools-biomarker-signature-studio for genomics and bioinformatics workflows.
Apply bio-research-tools-biomarker-signature-studio to sequencing, variant, or omics analysis tasks.