{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:59:40Z","timestamp":1785427180991,"version":"3.56.0"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Luxembourg Fondation Wivine"},{"name":"Luxembourg National Research Fund","award":["INTER\/JPND23\/17999421\/AD-PLCG2"],"award-info":[{"award-number":["INTER\/JPND23\/17999421\/AD-PLCG2"]}]},{"name":"Luxembourg National Research Fund","award":["INTER\/22\/17104370\/RECAST"],"award-info":[{"award-number":["INTER\/22\/17104370\/RECAST"]}]},{"name":"Luxembourg National Research Fund","award":["INTER\/EJP RD22\/17027921\/PreDYT"],"award-info":[{"award-number":["INTER\/EJP RD22\/17027921\/PreDYT"]}]},{"name":"Luxembourg National Research Fund","award":["C24\/BM\/18865990\/AsynIntact"],"award-info":[{"award-number":["C24\/BM\/18865990\/AsynIntact"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Understanding sex-dependent differences in disease risk, manifestation, and treatment response is essential for precision medicine. While funding agencies now mandate consideration of Sex as a Biological Variable (SABV), existing bioinformatics tools lack systematic approaches to characterize sex-related molecular mechanisms. Current practices frequently treat sex as a confounding variable, which may obscure important biological differences such as sex-specific alterations, sex-dimorphic changes (opposite effects between sexes), and sex-modulated changes (different effect magnitudes). We present\u00a0XYomics, an open-source R package for systematic analysis of sex-dependent alterations in biomedical omics data. The software identifies sex-specific, sex-dimorphic, and sex-modulated changes at both individual feature and systems levels. XYomics implements dual analytical modes: sex-disease interaction term modeling for adequately powered datasets and sex-stratified analysis with robust non-significance filtering for smaller sample sizes. Using single-cell RNA sequencing data from Alzheimer\u2019s disease patients, we demonstrate how XYomics identifies sex-dimorphic genes largely undetected by standard sex-averaged analyses. By integrating statistical categorization with pathway enrichment and network analysis using a curated hormone signaling interactome, the software facilitates discovery of sex-specific biomarkers and disease mechanisms frequently obscured in sex-aggregated analyses.<\/jats:p>","DOI":"10.1093\/nar\/gkag759","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:06:15Z","timestamp":1784721975000},"source":"Crossref","is-referenced-by-count":0,"title":["XYomics: detecting sex-dependent molecular mechanisms in omics data"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2173-7744","authenticated-orcid":false,"given":"Sophie","family":"Le\u00a0Bars","sequence":"first","affiliation":[{"name":"Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg , 6 Avenue du Swing, Belvaux 4367 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