{"id":7454,"date":"2017-05-19T13:25:17","date_gmt":"2017-05-19T17:25:17","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=7454"},"modified":"2017-05-22T15:22:54","modified_gmt":"2017-05-22T19:22:54","slug":"defining-the-gold-standard","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2017\/05\/defining-the-gold-standard\/","title":{"rendered":"Defining the &#8216;Gold Standard&#8217;"},"content":{"rendered":"<p>In their search for new ways to treat cancer, many scientists are using a high-tech process called genome sequencing to hunt for genetic mutations that encourage tumor cells to thrive. To aid in this search, some have developed new bioinformatics methods that claim to help pinpoint the cancer-friendly mutants.<\/p>\n<p>But a stubborn question remains: Among the numerous new tactics that aim to spotlight the so-called cancer driver genes, which produce the most accurate results?<\/p>\n<p>To help solve this puzzle, a team of Johns Hopkins computational scientists and cancer experts\u2014including doctoral student Collin Tokheim\u2014devised its own <a href=\"https:\/\/hub.jhu.edu\/2016\/12\/19\/bioinformatics-tool-evaluates-cancer-screening-strategies\/\" target=\"_blank\" rel=\"noopener noreferrer\">bioinformatics software<\/a> to evaluate how well the current strategies identify cancer-promoting mutations and distinguish them from benign mutations in cancer cells.<\/p>\n<p>The results of the researchers\u2019 analysis were published recently in <em>Proceedings of the National Academy of Sciences<\/em>.<\/p>\n<p>One of the challenges the team faced was the lack of a widely accepted consensus on what qualifies as a cancer driver gene. \u201cPeople have lists of what they consider to be cancer driver genes, but there\u2019s no official reference guide, no gold standard,\u201d says Tokheim, a lead author and researcher in the <a href=\"https:\/\/icm.jhu.edu\/\" target=\"_blank\" rel=\"noopener noreferrer\">Institute for Computational Medicine<\/a> and the <a href=\"https:\/\/www.bme.jhu.edu\/\" target=\"_blank\" rel=\"noopener noreferrer\">Department of Biomedical Engineering<\/a>.<\/p>\n<p>Nevertheless, Tokheim and his colleagues were able to develop a machine learning\u2013based method for driver gene prediction and a framework for evaluating and comparing other prediction methods. For the study, this evaluation tool was applied to eight existing cancer driver gene prediction methods.<\/p>\n<p>\u201cOur conclusion is that these methods still need to get better,\u201d says Tokheim, who was supervised by his doctoral adviser, <a href=\"https:\/\/www.bme.jhu.edu\/faculty_staff\/rachel-karchin-phd\/\" target=\"_blank\" rel=\"noopener noreferrer\">Rachel Karchin<\/a>, an associate professor of biomedical engineering and oncology, and senior author of the article. \u201cWe\u2019re sharing our methodology publicly, and it should help others to improve their systems for identifying cancer driver genes.\u201d<\/p>\n<hr \/>\n<h4>Web Extra<\/h4>\n<p><a href=\"http:\/\/bsun.md\/2p4H3xD\" target=\"_blank\" rel=\"noopener noreferrer\">Visit the Baltimore Sun website to watch a video.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A team of Johns Hopkins computational scientists and cancer experts devised its own bioinformatics software to evaluate how well current strategies identify cancer-promoting mutations and distinguish them from benign mutations in cancer cells.<\/p>\n","protected":false},"author":4,"featured_media":7922,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[25],"tags":[1638,1654,1650,1646,1642,121,110],"class_list":["post-7454","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-students","tag-rachel-karchin","tag-genome-sequencing","tag-cancer","tag-bioinformatics","tag-collin-tokheim","tag-department-of-biomedical-engineering","tag-institute-for-computational-medicine","issue-summer-2017"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - 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