bio-research-tools-biomarker-signature-studio

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---

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:

| 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). |

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.

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