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Anti\u2013tumor necrosis factor (anti\u2010<jats:styled-content style=\"fixed-case\">TNF<\/jats:styled-content>) drugs are an important second\u2010line treatment after methotrexate, the classic first\u2010line treatment for <jats:styled-content style=\"fixed-case\">RA<\/jats:styled-content>. However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti\u2010<jats:styled-content style=\"fixed-case\">TNF<\/jats:styled-content> drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using data on patient demographics, baseline disease assessment, treatment, and single\u2010nucleotide polymorphism (<jats:styled-content style=\"fixed-case\">SNP<\/jats:styled-content>) array from the Dialogue on Reverse Engineering Assessment and Methods (<jats:styled-content style=\"fixed-case\">DREAM<\/jats:styled-content>): Rheumatoid Arthritis Responder Challenge, we created a Gaussian process regression model to predict changes in the Disease Activity Score in 28 joints (<jats:styled-content style=\"fixed-case\">DAS<\/jats:styled-content>28) for the patients and to classify them into either the responder or the nonresponder group. This model was developed and cross\u2010validated using data from 1,892 <jats:styled-content style=\"fixed-case\">RA<\/jats:styled-content> patients. It was evaluated using an independent data set from 680 patients. We examined the effectiveness of the similarity modeling and the contribution of individual features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the cross\u2010validation tests, our method predicted changes in <jats:styled-content style=\"fixed-case\">DAS<\/jats:styled-content>28 (\u0394<jats:styled-content style=\"fixed-case\">DAS<\/jats:styled-content>28), with a correlation coefficient of 0.405. It correctly classified responses from 78% of patients. In the independent test, this method achieved a Pearson's correlation coefficient of 0.393 in predicting \u0394<jats:styled-content style=\"fixed-case\">DAS<\/jats:styled-content>28. Gaussian process regression effectively remapped the feature space and identified subpopulations that do not respond well to anti\u2010<jats:styled-content style=\"fixed-case\">TNF<\/jats:styled-content> treatments. Genetic <jats:styled-content style=\"fixed-case\">SNP<\/jats:styled-content> biomarkers showed small contributions in the prediction when added to the clinical models. This was the best\u2010performing model in the <jats:styled-content style=\"fixed-case\">DREAM<\/jats:styled-content> Challenge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The model described here shows promise in guiding treatment decisions in clinical practice, based primarily on clinical profiles with additional genetic information.<\/jats:p><\/jats:sec>","DOI":"10.1002\/art.41056","type":"journal-article","created":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T02:39:10Z","timestamp":1564022350000},"page":"1987-1996","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":129,"title":["Machine Learning to Predict Anti\u2013Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers"],"prefix":"10.1002","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8275-2852","authenticated-orcid":false,"given":"Yuanfang","family":"Guan","sequence":"first","affiliation":[{"name":"University of Michigan  Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjiu","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Michigan  Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Quang","sequence":"additional","affiliation":[{"name":"University of Michigan  Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyan","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Michigan  Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen C. 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