{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T17:46:16Z","timestamp":1782323176502,"version":"3.54.5"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Heart, Lung, and Blood Institute of the National Institutes of Health","award":["R01HL152735"],"award-info":[{"award-number":["R01HL152735"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https:\/\/bioneuralnet.readthedocs.io. Code archived at https:\/\/doi.org\/10.5281\/zenodo.17503083.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag365","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:32:59Z","timestamp":1781004779000},"source":"Crossref","is-referenced-by-count":0,"title":["BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9391-6091","authenticated-orcid":false,"given":"Vicente","family":"Ramos","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Colorado Denver , Denver, CO,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5994-7480","authenticated-orcid":false,"given":"Sundous","family":"Hussein","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Colorado Denver , Denver, CO,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Abdel-Hafiz","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Colorado Denver , Denver, CO,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0648-4750","authenticated-orcid":false,"given":"Arunangshu","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0222-1784","authenticated-orcid":false,"given":"Weixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3725-5459","authenticated-orcid":false,"given":"Katerina J","family":"Kechris","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4651-363X","authenticated-orcid":false,"given":"Russell P","family":"Bowler","sequence":"additional","affiliation":[{"name":"Genomic Medicine Institute, Cleveland Clinic Main Campus , Cleveland, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leslie","family":"Lange","sequence":"additional","affiliation":[{"name":"Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus , Aurora, CO,","place":["United 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