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Dr. Vikas Pejaver is an Assistant Professor at the Institute for Genomic Health<\/a> and the Department of Genetics and Genomic Sciences<\/a> in the Icahn School of Medicine at Mount Sinai<\/a>. His research focuses on the development and application of machine learning methods to relate genetic variation to molecular function and disease phenotypes, with a particular emphasis on rare variants and diseases.\u00a0His work utilizes a broad array of machine learning techniques on genomic, protein and electronic health record data sets. Dr. Pejaver has a\u00a0Bachelor\u2019s degree in Biotechnology from the People\u2019s Education Society (PES) Institute of Technology (now PES University<\/a>) in Bengaluru, India. After that, he received his Master\u2019s degree in Bioinformatics and doctoral degree in Informatics from the School of Informatics and Computing (now School of Informatics, Computing and Engineering<\/a>) at Indiana University, Bloomington.\u00a0Dr. Pejaver then completed his postdoctoral training at the <\/span>Department of Biomedical Informatics and Medical Education<\/a>\u00a0(BIME) and the\u00a0<\/span>eScience Institute<\/a>\u00a0at the <\/span>University of Washington<\/a> (UW), where he received the Moore\/Sloan<\/span>\u00a0and Washington Research Foundation Innovation in Data Science Postdoctoral Fellowship. He also received<\/span>\u00a0a K99\/R00 Pathway to Independence Award from the <\/span>National Library of Medicine<\/a> at the <\/span>National Institutes of Health<\/a>. At UW, he was also awarded the Fred Wolf Mentorship Award for\u00a0his active roles in training and mentoring students in BIME.<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Principal InvestigatorVikas Pejaver Dr. Vikas Pejaver is an Assistant Professor at the Institute for Genomic Health and the Department of Genetics and Genomic Sciences in the Icahn School of Medicine at Mount Sinai. His research focuses on the development and application of machine learning methods to relate genetic variation to molecular function and disease phenotypes, […]<\/p>\n","protected":false},"author":503,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-25","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/pages\/25","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/users\/503"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/comments?post=25"}],"version-history":[{"count":6,"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/pages\/25\/revisions"}],"predecessor-version":[{"id":303,"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/pages\/25\/revisions\/303"}],"wp:attachment":[{"href":"https:\/\/labs.icahn.mssm.edu\/pejaverlab\/wp-json\/wp\/v2\/media?parent=25"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}