{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:49:22Z","timestamp":1773100162597,"version":"3.50.1"},"reference-count":77,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220530152409020"],"award-info":[{"award-number":["JCYJ20220530152409020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations between gut microbiota and disease states as well as other intrinsic or environmental factors. However, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe\u2013microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We proposed a novel method to identify underlying microbial modules, i.e. groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intragroup relationships, indicating potential microbe\u2013microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust microbial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/rwang-z\/microbial_module.git.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad213","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T18:53:52Z","timestamp":1682103232000},"source":"Crossref","is-referenced-by-count":7,"title":["Deciphering associations between gut microbiota and clinical factors using microbial modules"],"prefix":"10.1093","volume":"39","author":[{"given":"Ran","family":"Wang","sequence":"first","affiliation":[{"name":"Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University , Shenzhen 518020, China"},{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong"}]},{"given":"Xubin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University , Shenzhen 518020, China"},{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong"}]},{"given":"Fangda","family":"Song","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong , Shenzhen 518000, China"}]},{"given":"Man Hon","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong"}]},{"given":"Kwong Sak","family":"Leung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong"},{"name":"Department of Applied Data Science, Hong Kong Shue Yan University , North Point, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9427-383X","authenticated-orcid":false,"given":"Lixin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University , Shenzhen 518020, China"},{"name":"Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics , Shenzhen 518020, 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