{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hanassab S"],"funding":["European Research Council","National Institute for Health Research (NIHR)","Vodafone Foundation","National Institute for Health and Care Research","UK Research and Innovation","Engineering and Physical Sciences Research Council"],"pagination":["68"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12669042"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["3(1)"],"pubmed_abstract":["Polycystic ovary syndrome (PCOS) is a complex, multifactorial, and polygenic disorder. Here, we employed machine learning (ML) techniques to analyze large open-source datasets to identify bioactive molecules in foods and pharmacological agents that interact with genes and biological functions central to PCOS pathophysiology. We selected 13 PCOS-associated genes as targets, and the network propagation algorithm systematically identified bioactive molecules that interact with pathways relevant to PCOS. Among the top-ranked molecules, epicatechin-3-gallate (found in green tea) and 24-methylenecycloartan-3-ol (found in almonds) were newly identified, with green tea and almonds previously demonstrated to have anti-androgenic and anti-inflammatory properties. Validation of the ML pipeline with clinically available drugs revealed significant interactions with gonadotropin-releasing hormone receptor modulators, consistent with their established role in PCOS pathophysiology. These findings identify novel therapeutic targets for further research in precision nutrition and drug repurposing for PCOS treatment."],"journal":["npj women's health"],"pubmed_title":["Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning."],"pmcid":["PMC12669042"],"funding_grant_id":["NIHR304591","EP/X040062/1","EP/S023283/1","NIHR202371","CS-2018-18-ST2-002","899932"],"pubmed_authors":["Laponogov I","Bronstein M","Hanassab S","Izzi-Engbeaya C","Dhillo WS","Olabode AV","Heinis T","Abbara A","Veselkov K","Southern J","Comninos AN"],"additional_accession":[]},"is_claimable":false,"name":"Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning.","description":"Polycystic ovary syndrome (PCOS) is a complex, multifactorial, and polygenic disorder. Here, we employed machine learning (ML) techniques to analyze large open-source datasets to identify bioactive molecules in foods and pharmacological agents that interact with genes and biological functions central to PCOS pathophysiology. We selected 13 PCOS-associated genes as targets, and the network propagation algorithm systematically identified bioactive molecules that interact with pathways relevant to PCOS. Among the top-ranked molecules, epicatechin-3-gallate (found in green tea) and 24-methylenecycloartan-3-ol (found in almonds) were newly identified, with green tea and almonds previously demonstrated to have anti-androgenic and anti-inflammatory properties. Validation of the ML pipeline with clinically available drugs revealed significant interactions with gonadotropin-releasing hormone receptor modulators, consistent with their established role in PCOS pathophysiology. These findings identify novel therapeutic targets for further research in precision nutrition and drug repurposing for PCOS treatment.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-25T03:11:42.284Z","creation":"2026-06-25T03:07:38.99Z"},"accession":"S-EPMC12669042","cross_references":{"pubmed":["41341431"],"doi":["10.1038/s44294-025-00117-4"]}}