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tooluniverse-multiomic-disease-characterization

Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.

skills/tooluniverse-multiomic-disease-characterization/SKILL.md

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Last checked: Nov 9, 2026

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Repository stars (not skill ratings): 1,780
Repository last updated: Nov 9, 2026
License: Apache-2.0

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.env.templateasset · 499 bytes
SKILL.mdcore · 8,911 bytes
integration-scoring.mddoc · 4,491 bytes
report-template.mddoc · 7,546 bytes
response-formats.mddoc · 2,415 bytes
test_multiomic_disease.pyruns · 45,094 bytes
tool-reference.mddoc · 16,089 bytes
use-patterns.mddoc · 3,592 bytes

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# Multi-Omics Disease Characterization Pipeline

Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.

**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Disease disambiguation FIRST** - Resolve all identifiers before omics analysis
3. **Layer-by-layer analysis** - Systematically cover all omics layers
4. **Cross-layer integration** - Identify genes/targets appearing in multiple layers
5. **Evidence grading** - Grade all evidence as T1 (human/clinical) to T4 (computational)
6. **Tissue context** - Emphasize disease-relevant tissues/organs
7. **Quantitative scoring** - Multi-Omics Confidence Score (0-100)
8. **Druggable focus** - Prioritize targets with therapeutic potential
9. **Biomarker identification** - Highlight diagnostic/prognostic markers