{"doi":"10.1093/bioinformatics/btac735","title":"Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19","abstract":"MOTIVATION: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. RESULTS: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. AVAILABILITY AND IMPLEMENTATION: GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2022,"id":276010,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9529,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":946095,"name":"Taryn O. Hall","orcid":"0000-0001-6018-8613","position":1,"is_corresponding":false},{"id":550683,"name":"Yinglong Guo","orcid":"0000-0003-3456-4077","position":2,"is_corresponding":false},{"id":946636,"name":"Luke Tso","orcid":null,"position":3,"is_corresponding":false},{"id":946637,"name":"Kathy Tzeng","orcid":null,"position":4,"is_corresponding":false},{"id":15696,"name":"Le Cong","orcid":"0000-0003-4725-8714","position":5,"is_corresponding":false},{"id":3452,"name":"Russ B. Altman","orcid":"0000-0003-3859-2905","position":6,"is_corresponding":false},{"id":946094,"name":"Henry C. Cousins","orcid":"0000-0002-8694-0604","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T00:28:26.545826Z","pmid":"36394254","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}