{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T19:41:16Z","timestamp":1784922076207,"version":"3.55.0"},"reference-count":166,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Elite Medical Professionals project of China-Japan Friendship Hospital","award":["ZRJY2021-BJ07"],"award-info":[{"award-number":["ZRJY2021-BJ07"]}]}],"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>The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.<\/jats:p>","DOI":"10.1093\/bib\/bbae364","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:56:13Z","timestamp":1722477373000},"source":"Crossref","is-referenced-by-count":47,"title":["Integrated multi-omics with machine learning to uncover the intricacies of kidney disease"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5252-7668","authenticated-orcid":false,"given":"Xinze","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing University of Chinese Medicine , China-Japan Friendship Clinic Medical College, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxuan","family":"Shi","sequence":"additional","affiliation":[{"name":"China Japan Friendship Institute of Clinical Medicine Research , Department of Nephrology, China-Japan Friendship Hospital, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Jiao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College Fuwai Hospital , Department of Nephrology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"An","sequence":"additional","affiliation":[{"name":"Peking University China-Japan Friendship School of Clinical Medicine , Department of Nephrology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Tian","sequence":"additional","affiliation":[{"name":"China-Japan Friendship Hospital , Department of Nephrology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[{"name":"China-Japan Friendship Hospital , Department of Nephrology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhuo","sequence":"additional","affiliation":[{"name":"China-Japan Friendship Hospital , Department of Nephrology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"2024092601104028600_ref1","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/S0140-6736(18)31694-5","article-title":"Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories","volume":"392","author":"Foreman","year":"2018","journal-title":"Lancet"},{"key":"2024092601104028600_ref2","article-title":"Advances in the management of chronic kidney 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