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However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European\/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In both populations, models were able to predict JIA status well, with training accuracies &gt;\u200974% and testing accuracies &gt;\u200978%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13075-019-2010-z","type":"journal-article","created":{"date-parts":[[2019,11,9]],"date-time":"2019-11-09T12:03:13Z","timestamp":1573300993000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches"],"prefix":"10.1186","volume":"21","author":[{"given":"Kerry E.","family":"Poppenberg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carol A.","family":"Wallace","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Hennon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-0717","authenticated-orcid":false,"given":"James N.","family":"Jarvis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,9]]},"reference":[{"issue":"11","key":"2010_CR1","doi-asserted-by":"publisher","first-page":"3554","DOI":"10.1002\/art.21389","volume":"52","author":"CA Wallace","year":"2005","unstructured":"Wallace CA, Huang B, Bandeira M, Ravelli A, Giannini EH. 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All research was conducted according to the IRB-approved protocol, and informed consent was obtained from all subjects.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No specific patient information\/images are presented in the manuscript. Consent documents for this study explicitly state that data will be published but that individual subjects will remain anonymous.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"KEP, KJ, LL, YS, CAW, TH, and JNJ declare that they have no competing interests. HM is a principal investigator: three Brain Aneurysm Foundation grants (no applicable grant numbers), NIH Grants R01-NS-091075 and R01-NS-064592, and Grant support Canon Medical Systems. HM is a co-founder of Neurovascular Diagnostics, Inc.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"230"}}