Connect biological knowledge

This part treats GO, KEGG, Reactome, disease, MeSH, pathway collections, and custom annotations as knowledge sources for one common analysis system. The analysis engine from Part 1 remains the same while the vocabulary, annotation coverage, update schedule, and biological interpretation change.

How to choose a knowledge source

Need	Start with
Broad ontology-based functional coverage	GO
Curated pathways, modules, or compounds	KEGG or Reactome
Disease or phenotype concepts	Disease enrichment
Literature-oriented biomedical concepts	MeSH
A project-specific, unsupported, or multi-resource collection	Universal enrichment and GSON
Genomic regions that must first be mapped to genes	Genomic coordination enrichment

The shared knowledge contract

Every source should make its term-to-gene mapping, term names, gene identifier type, gene set identifier domain, organism, release, and filtering rules explicit. A two-column TERM2GENE table is enough for an exploratory analysis; a versioned GSON object is preferable when the knowledge will be reused, shared, combined, or cited, because it carries these declarations as slots rather than leaving them in the notebook that produced it.

The domain chapters use the same pattern:

biological question → source and identifier contract → ORA/GSEA
                   → result object → visualization → limitations

Knowledge sources are not interchangeable evidence. GO terms, KEGG pathways, disease terms, and WikiPathways entries can differ in granularity and coverage. Combined plots should keep the source identity visible.