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Biol."],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Genome\u2013phenome association (GPA) prediction can broaden the understanding of biological mechanisms underlying complex phenotypic traits (e.g.,\u00a0diseases and agronomic traits). Traditional deep matrix factorization (DMF)\u2010based GPA methods can integrate multiple data types and uncover nonlinear associations but often rely on low\u2010dimensional matrix representations, making it challenging to capture intricate node interactions under sparse data conditions. Existing approaches also inadequately integrate multi\u2010omics data and frequently neglect local neighborhood (LN) and graph structural information, particularly when handling incomplete or low\u2010quality attribute data. Furthermore, most methods fail to effectively address the scarcity of plant phenotype data. We propose GDMF, an end\u2010to\u2010end GPA prediction framework that integrates a multisource graph attention network (GAT) with weighted DMF. By constructing a heterogeneous molecular network, GDMF unifies multi\u2010omics data and utilizes GAT\u2019s attention mechanism to process multisource associations (both cross\u2010type and intra\u2010type node interactions), thereby enhancing the capture of LN structures while preserving critical node information. The weighted DMF component dynamically assigns importance weights to global relationship matrices, enabling robust modeling of complex gene\u2013phenotype interactions even with sparse and noisy attribute data. To mitigate the scarcity of plant phenotype data, GDMF incorporates textual semantic similarity of trait ontology terms to enrich phenotypic attribute representations. Extensive experiments on maize and human datasets demonstrate that GDMF significantly outperforms competitive methods, validating its efficacy and superiority in GPA prediction.<\/jats:p>","DOI":"10.1002\/qub2.70044","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T03:50:32Z","timestamp":1781841032000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Genome\u2013phenome association prediction using weighted deep matrix factorization with a multisource graph attention network"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2927-3857","authenticated-orcid":false,"given":"Ran","family":"Duan","sequence":"first","affiliation":[{"name":"School of Intelligence Science and Technology Beijing University of Civil Engineering and Architecture  Beijing China"},{"name":"Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture 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