{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T13:44:47Z","timestamp":1777470287872,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100010336","name":"Hellman Foundation","doi-asserted-by":"publisher","award":["Hellman Fellows Fund"],"award-info":[{"award-number":["Hellman Fellows Fund"]}],"id":[{"id":"10.13039\/100010336","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R34DK102100"],"award-info":[{"award-number":["R34DK102100"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010511","name":"Clinical and Translational Science Institute, University of California, San Francisco","doi-asserted-by":"publisher","award":["UL1TR001872"],"award-info":[{"award-number":["UL1TR001872"]}],"id":[{"id":"10.13039\/100010511","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Biological Research For Nursing"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:p>Background: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases.<\/jats:p>\n                  <jats:p>Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics.<\/jats:p>\n                  <jats:p>Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov\u2013Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types.<\/jats:p>\n                  <jats:p>Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.<\/jats:p>","DOI":"10.1177\/10998004221147513","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T23:29:06Z","timestamp":1672874946000},"page":"393-403","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes"],"prefix":"10.1177","volume":"25","author":[{"given":"Lisa","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Physiological Nursing, University of California San Francisco, San Francisco, CA, USA"},{"name":"Keck Graduate Institute, Claremont, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshimi","family":"Fukuoka","sequence":"additional","affiliation":[{"name":"Department of Physiological Nursing, University of California San Francisco, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bradley E.","family":"Aouizerat","sequence":"additional","affiliation":[{"name":"Bluestone Center for Clinical Research, New York University, New York, NY, USA"},{"name":"Department of Oral and Maxillofacial Surgery, New York University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA"},{"name":"Department of Medicine, University of California San Francisco, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7054-7533","authenticated-orcid":false,"given":"Elena","family":"Flowers","sequence":"additional","affiliation":[{"name":"Department of Physiological Nursing, University of California San Francisco, San Francisco, CA, USA"},{"name":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"bibr1-10998004221147513","volume-title":"scikit-learn","author":"1.11.Ensemble methods"},{"key":"bibr2-10998004221147513","doi-asserted-by":"publisher","DOI":"10.5210\/ojphi.v12i1.10611"},{"key":"bibr3-10998004221147513","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.09.127"},{"key":"bibr4-10998004221147513","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194127"},{"key":"bibr5-10998004221147513","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008671"},{"key":"bibr6-10998004221147513","volume-title":"The WEKA Workbench. 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