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Multi\u2010omics data play a crucial role in uncovering complex biological processes, yet simultaneous representation learning of such high\u2010dimensional, heterogeneous multi\u2010modality data along with clinical outcomes remains limited. To address this gap, we propose a supervised knowledge\u2010guided Bayesian factor model for integrative analysis of multi\u2010omics and clinical outcome data. The proposed method simultaneously extracts an informative low\u2010dimensional representation and predicts one or more clinical outcomes of interest. The two\u2010level adaptive shrinkage in the novel hierarchical priors allows for the identification of both active modalities and features, resulting in a biologically meaningful structural identification of the high\u2010dimensional data. Moreover, the method is robust to noisy edges in biological graphs that do not align with ground truth. Finally, the proposed method can handle different data types including both continuous and categorical data. Extensive simulation studies and real data analyses of Alzheimer's disease (AD) data demonstrate the advantages of the proposed approach over existing methods. Notably, our analysis of multi\u2010omics and imaging phenotype data from ADNI provides meaningful insights into the underlying biological mechanisms of AD.<\/jats:p>","DOI":"10.1002\/sim.70570","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T11:56:34Z","timestamp":1777204594000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simultaneous Representation Learning of Multi\u2010Omics and Clinical Outcome Data via a Supervised Knowledge\u2010Guided Bayesian Factor Model"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6555-4640","authenticated-orcid":false,"given":"Qiyiwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania 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