DOSE provides disease ontology semantic similarity and enrichment analysis for biomedical knowledge mining. It supports ontology-aware interpretation of genes, gene clusters, and ranked gene lists by connecting user-provided genes to disease, phenotype, and cancer gene-set annotations.
The package currently focuses on two analysis layers:
DOSE uses curated ontology and gene annotation resources prepared by package data-building scripts.
Ontology-backed resources are stored in SQLite databases and support semantic similarity:
Gene-set resources that are only used for enrichment analysis are represented as gene-set collections, including cancer gene sets from the Network of Cancer Genes (NCG).
DOSE implements classic semantic similarity methods for ontology terms and gene products, including Resnik, Lin, Jiang, Schlicker, and Wang methods where supported by the ontology data.
For enrichment analysis, DOSE provides both over-representation analysis and gene set enrichment analysis interfaces. These methods allow researchers to interpret gene vectors or ranked gene lists in disease and phenotype contexts.
This vignette is intentionally brief. Full documentation, worked examples, and case studies are maintained in the online book:
https://yulab-smu.top/biomedical-knowledge-mining-book/
If you use DOSE in published research, please cite:
Yu G, Wang L-G, Yan G-R, He Q-Y. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609. https://doi.org/10.1093/bioinformatics/btu684
Please post questions and issues to the Bioconductor support site and tag the post with DOSE.