{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:35:23Z","timestamp":1771230923105,"version":"3.50.1"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hong Kong Special Administrative Region","award":["T12-710\/16-R"],"award-info":[{"award-number":["T12-710\/16-R"]}]},{"name":"Hong Kong Special Administrative Region","award":["14303819"],"award-info":[{"award-number":["14303819"]}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","award":["MHP\/033\/20"],"award-info":[{"award-number":["MHP\/033\/20"]}],"id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong PhD Fellowship Scheme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Compared with analyzing omics data from a single platform, an integrative analysis of multi-omics data provides a more comprehensive understanding of the regulatory relationships among biological features associated with complex diseases. However, most existing frameworks for integrative analysis overlook two crucial aspects of multi-omics data. Firstly, they neglect the known dependencies among biological features that exist in highly credible biological databases. Secondly, most existing integrative frameworks just simply remove the subjects without full omics data to handle block missingness, resulting in decreasing statistical power. To overcome these issues, we propose a network-based integrative Bayesian framework for biomarker selection and disease outcome prediction based on multi-omics data. Our framework utilizes Dirac spike-and-slab variable selection prior to identifying a small subset of biomarkers. The incorporation of gene pathway information improves the interpretability of feature selection. Furthermore, with the strategy in the FBM (stand for \u201dfull Bayesian model with missingness\u201d) model where missing omics data are augmented via a mechanistic model, our framework handles block missingness in multi-omics data via a data augmentation approach. The real application illustrates that our approach, which incorporates existing gene pathway information and includes subjects without DNA methylation data, results in more interpretable feature selection results and more accurate predictions.<\/jats:p>","DOI":"10.1093\/bib\/bbae454","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T17:27:31Z","timestamp":1726594051000},"source":"Crossref","is-referenced-by-count":3,"title":["NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction"],"prefix":"10.1093","volume":"25","author":[{"given":"Bencong","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong SAR, China"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong SAR, China"}]},{"given":"Suet Yi","family":"Leung","sequence":"additional","affiliation":[{"name":"Department of Pathology , School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2744-9030","authenticated-orcid":false,"given":"Xiaodan","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong SAR, China"}]}],"member":"286","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"2024091717262877700_ref1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa169","article-title":"Vertical integration methods for gene expression data analysis","volume":"22","author":"Mengyun","year":"2021","journal-title":"Brief 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