{"id":63426,"date":"2019-03-22T09:00:10","date_gmt":"2019-03-22T13:00:10","guid":{"rendered":"http:\/\/webapp2.wright.edu\/web1\/newsroom\/?p=63426"},"modified":"2019-03-22T10:10:31","modified_gmt":"2019-03-22T14:10:31","slug":"top-detective","status":"publish","type":"post","link":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/2019\/03\/22\/top-detective\/","title":{"rendered":"Top detective"},"content":{"rendered":"<div id=\"attachment_63442\" style=\"width: 470px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/webapp2.wright.edu\/web1\/newsroom\/2019\/03\/21\/top-detective\/20975-jim-hannah-computer-science-student-soonjye-kho-3-5-19\/\" rel=\"attachment wp-att-63442\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-63442\" class=\"size-large wp-image-63442\" src=\"http:\/\/webapp2.wright.edu\/web1\/newsroom\/files\/2019\/03\/soon-jye-kho-20975_027-1-508x312.jpg\" alt=\"\" width=\"460\" height=\"283\" \/><\/a><p id=\"caption-attachment-63442\" class=\"wp-caption-text\">Soon Jye Kho, a computer science Ph.D. student in Wright State&#8217;s Kno.e.sis Center, was recognized\u00a0at the precisionFDA Mislabeling Correction Subchallenge. (Photo by Erin Pence)<\/p><\/div>\n<p>Wright State University <a href=\"https:\/\/engineering-computer-science.wright.edu\/computer-science-and-engineering\/phd-in-computer-science-and-engineering\">computer science Ph.D.<\/a> student Soon Jye Kho was named one of the top performers in an international research challenge by the U.S. Food and Drug Administration designed to detect medical mislabeling through computer analysis.<\/p>\n<p>Kho\u2019s entry was among the three top-performing submissions out of 82 submitted by academic and private researchers from around the world in the <a href=\"https:\/\/precision.fda.gov\/challenges\/5\/view\/results\">precisionFDA Mislabeling Correction Subchallenge<\/a>.<\/p>\n<p>Researchers taking part in the challenge hailed from Denmark, Korea, Luxembourg as well as the Cleveland Clinic and U.S. universities, including Texas Tech and the University of Michigan.<\/p>\n<p>\u201cI feel excited about it and kind of proud,\u201d said Kho, whose research area is using computer science to analyze medical data. \u201cI\u2019ve been able to utilize what I\u2019ve learned in a practical real-life scenario.\u201d<\/p>\n<p>Kho, who grew up in Malaysia, is a Ph.D. student at <a href=\"http:\/\/knoesis.org\/\">Kno.e.sis, Wright State\u2019s Ohio Center of Excellence in Knowledge-Enabled Computing<\/a>. His adviser is Amit Sheth, the LexisNexis Ohio Eminent Scholar, a professor of <a href=\"https:\/\/engineering-computer-science.wright.edu\/computer-science-and-engineering\">computer science and engineering<\/a>, and the executive director of Kno.e.sis.<\/p>\n<p>\u201cKho\u2019s win is significant in two ways,\u201d said Sheth. \u201cFirst, it represents one more win for a Kno.e.sis student in a national or an international level competition. Second, it is an important addition to our growing body of research in precision medicine and personalized digital health, topics of significant importance for Kno.e.sis\u2019 role as an Ohio Center of Excellence in BioHealth Innovation.\u201d<\/p>\n<p>The online data challenge occurred from November to December. Researchers participated remotely. Top performers were announced in February.<\/p>\n<p>FDA challenges are designed to find solutions to real-world problems. The objective of the mislabeling challenge was to encourage development and evaluation of computational algorithms that can accurately detect and correct mislabeled samples.<\/p>\n<p>The accidental swapping of patient tissue samples or genetic data can contribute to invalid conclusions and wrong or ineffective treatments.<\/p>\n<p>\u201cWhen a sample belongs to a healthy individual but is mislabeled as cancer tissue, the physician might prescribe unnecessary treatment to the individual, which could harm them,\u201d said Kho.<\/p>\n<p>Participants in the challenge were presented with 160 tumor samples, with about 15 percent of them containing labeling errors. They were asked to create computational algorithms to model the relationship between clinical attributes, protein profiles and mRNA profiles using the data, then to apply the model to identify and correct mislabeled samples.<\/p>\n<p>\u201cWe employed machine-learning techniques where we train the machine to see the pattern in a cancerous genetic profile and the normal genetic profile,\u201d said Kho. \u201cSo if there is mismatch between machine prediction and a patient\u2019s diagnosis, mislabeling is suspected and we could go back to reaffirm the labeling before performing any downstream analysis.\u201d<\/p>\n<p>Computer analysis is becoming a powerful approach to understanding disease and speeding the translation of new discoveries in the labs to patient care.<\/p>\n<p>Kho believes the outcome of the challenge can have a real impact. After earning his Ph.D., he would like to conduct research in academia or the private sector.<\/p>\n<p>\u201cI\u2019m interested in precision medicines and translational research that speeds up findings in the lab to the clinical side,\u201d he said.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Soon Jye Kho, a computer science Ph.D. student in Wright State&#8217;s Kno.e.sis Center, was recognized at the precisionFDA Mislabeling Correction Subchallenge. <a href=\"https:\/\/webapp2.wright.edu\/web1\/newsroom\/2019\/03\/22\/top-detective\/\" class=\"morelink\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":17,"featured_media":63446,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[722,4267,743,2060,725,715,18],"tags":[],"class_list":["post-63426","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-academics","category-computer-science-and-engineering","category-engineering-computer-science","category-graduate","category-home-news-sidebar","category-news","category-research"],"_links":{"self":[{"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/posts\/63426","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/comments?post=63426"}],"version-history":[{"count":5,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/posts\/63426\/revisions"}],"predecessor-version":[{"id":63462,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/posts\/63426\/revisions\/63462"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/media\/63446"}],"wp:attachment":[{"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/media?parent=63426"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/categories?post=63426"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/webapp2.wright.edu\/web1\/newsroom\/wp-json\/wp\/v2\/tags?post=63426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}