How do I install the Bioresearch Disease Case Study skill?

Run npx skillmds@latest add alim430/bioresearch-disease-case-study in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Bioresearch Disease Case Study free to use?

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Who published Bioresearch Disease Case Study?

Alim430 (@alim430) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bioresearch Disease Case Study by alim430

Name: Bioresearch Disease Case Study
Author: Alim430

Bioresearch Disease Case Study

Run a reproducible, real-public-data disease case study that composes the literature → biomarker → causal workflows end-to-end and emits a blind benchmark evaluation (known-gene recovery, pathway sanity, reproducibility hash). Use when the user wants to prove the framework on a real disease (e.g. Parkinson's GSE7621) or asks for a "case study", "validation run", or "benchmark" of the biomedical workflows.

Alim430 Updated Sep 17, 2026

File contents

BioResearch Agent — Disease Case Study Skill

Capability

A workflow-composition + validation skill. It runs a real, public GEO dataset through the framework's analysis engine (differential expression → pathway enrichment → candidate ranking, with optional literature and causal-inference stages) and produces a blind benchmark evaluation:

Known-gene recovery — how many established disease genes surface in the top-ranked candidates (e.g. SNCA / LRRK2 / PARK7 / PINK1 / PRKN / GBA for Parkinson's).
Pathway sanity — whether disease-relevant pathways (dopamine / mitochondrial / oxidative / immune) are enriched.
Reproducibility — commit hash, environment, and data sha256 recorded in an Evidence Package.

Case Study 1 ships with the suite: Parkinson's disease / GSE7621 (GPL570, 25 samples). See bio-research-os/eval/case_study_pd.py and the Biomedical Workflow Validation Suite README.

Run

python bio-research-os/eval/case_study_pd.py \
    --matrix-path downloads/gse7621_matrix.txt.gz \
    --annot-path  downloads/gpl570.annot.gz \
    --output-dir  docs/case-study

(If paths are omitted the runner downloads the real GSE7621 matrix + GPL570 annotation itself.)

Outputs (in `docs/case-study/`)

GSE7621_deg.csv — differential expression results
GSE7621_top_candidates.csv — ranked biomarker candidates
GSE7621_pathway_enrichment.csv — enriched KEGG / GO terms
GSE7621_volcano.png — volcano plot
GSE7621_report.txt — structured report incl. blind benchmark
GSE7621_evidence_package.json — provenance + benchmark + limitations

Note

This skill is a composition & validation interface. It adds no statistics of its own — every number comes from the framework's workflow modules. It is the honest way to demonstrate the framework works on real data (not synthetic injection). Requires no LLM key.

---name: bioresearch-disease-case-studydescription: Run a reproducible, real-public-data disease case study that composes the literature → biomarker → causal workflows end-to-end and emits a blind benchmark evaluation (known-gene recovery, pathway sanity, reproducibility hash). Use when the user wants to prove the framework on a real disease (e.g. Parkinson's GSE7621) or asks for a "case study", "validation run", or "benchmark" of the biomedical workflows.---# BioResearch Agent — Disease Case Study Skill## CapabilityA **workflow-composition + validation** skill. It runs a real, public GEO dataset through theframework's analysis engine (differential expression → pathway enrichment → candidate ranking,with optional literature and causal-inference stages) and produces a **blind benchmarkevaluation**:- **Known-gene recovery** — how many established disease genes surface in the top-ranked candidates (e.g. SNCA / LRRK2 / PARK7 / PINK1 / PRKN / GBA for Parkinson's).- **Pathway sanity** — whether disease-relevant pathways (dopamine / mitochondrial / oxidative / immune) are enriched.- **Reproducibility** — commit hash, environment, and data `sha256` recorded in an Evidence Package.Case Study 1 ships with the suite: **Parkinson's disease / GSE7621** (GPL570, 25 samples).See `bio-research-os/eval/case_study_pd.py` and the Biomedical Workflow Validation Suite README.## Run```bashpython bio-research-os/eval/case_study_pd.py \ --matrix-path downloads/gse7621_matrix.txt.gz \ --annot-path downloads/gpl570.annot.gz \ --output-dir docs/case-study```(If paths are omitted the runner downloads the real GSE7621 matrix + GPL570 annotation itself.)## Outputs (in `docs/case-study/`)- `GSE7621_deg.csv` — differential expression results- `GSE7621_top_candidates.csv` — ranked biomarker candidates- `GSE7621_pathway_enrichment.csv` — enriched KEGG / GO terms- `GSE7621_volcano.png` — volcano plot- `GSE7621_report.txt` — structured report incl. blind benchmark- `GSE7621_evidence_package.json` — provenance + benchmark + limitations## NoteThis skill is a **composition & validation interface**. It adds no statistics of its own — everynumber comes from the framework's workflow modules. It is the honest way to demonstrate theframework works on real data (not synthetic injection). Requires no LLM key.

Frequently asked questions