name: bio-research-tools-biomarker-signature-studio description: Multi-omic biomarker discovery studio that ingests expression + metadata, performs QC, multi-strategy feature selection, nested CV model training, survival analysis hooks, and SHAP-based interpretation. Use to design translational biomarker panels with documented evidence. tool_type: python primary_tool: scikit-learn depends_on:

• machine-learning/biomarker-discovery
• machine-learning/model-validation
• machine-learning/omics-classifiers
• differential-expression/de-results
• workflow-management/biomarker-pipeline measurable_outcome: Run biomarker_signature_studio.py end-to-end on provided data within 20 minutes and produce metrics + feature rankings JSON artifacts. allowed-tools:
• read_file
• run_shell_command

Biomarker Signature Studio

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.

What This Skill Does

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

Inputs

• 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.

Quick CLI Usage

python Skills/Research_Tools/Biomarker_Signature_Studio/scripts/biomarker_signature_studio.py \
  --expression data/expression.csv \
  --metadata data/metadata.csv \
  --label-column phenotype \
  --selectors boruta,lasso,mrmr \
  --models rf,logit \
  --output-dir outputs/biomarkers_run1

Key flags:

--selectors

boruta

lasso

mrmr

mi_topk

--models

logit

rf

xgb

--k-features

mrmr

mi_topk

--survival

--random-state

--nested-folds

Workflow

1. Load + align inputs, infer orientation, impute missing values.
2. Standardize features (fit on train set only).
3. Run requested selectors; create intersection + union candidate lists.
4. For each selector output run nested CV training across requested models.
5. Export champion metrics (

   metrics.json

   ), feature table (

   selected_features.csv

   ), SHAP summary (

   shap_summary.csv

   when available), and survival stats (

   survival.json

   ).

QC Expectations

• Class count ratio ≤3:1; warnings logged otherwise.
• Selected features between 5 and 250 unless user overrides.
• Nested CV AUC ≥0.70 or flagged in report.
• SHAP overlap with selected features ≥60% (reported).

Related Assets

• examples/configs/biomarker_studio_template.yaml

  (scaffold for teams)

• scripts/biomarker_signature_studio.py

  (entry point)
• Existing biomarker workflow skill for orchestrated runs.

Use this skill whenever you need a ready-to-review biomarker dossier (data QC, model metrics, explainability artifacts) before moving to validation cohorts or lab assays.