{"id":3248,"date":"2020-12-02T17:59:32","date_gmt":"2020-12-02T10:59:32","guid":{"rendered":"http:\/\/research.binus.ac.id\/bdsrc\/?p=3248"},"modified":"2023-07-22T13:16:26","modified_gmt":"2023-07-22T06:16:26","slug":"a-design-of-polygenic-risk-model-with-deep-learning-for-colorectal-cancer-in-multiethnic-indonesians","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/bdsrc\/2020\/12\/02\/a-design-of-polygenic-risk-model-with-deep-learning-for-colorectal-cancer-in-multiethnic-indonesians\/","title":{"rendered":"A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians"},"content":{"rendered":"<p>Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.<\/p>\n<p>International Conference on Computer Science and Computational Intelligence 2020<\/p>\n<p><strong>Steven Amadeus, Tjeng Wawan Cenggoro, Arif Budiarto, and Bens Pardamean<\/strong><\/p>\n<p><a href=\"https:\/\/www.researchgate.net\/publication\/349489926_A_Design_of_Polygenic_Risk_Model_with_Deep_Learning_for_Colorectal_Cancer_in_Multiethnic_Indonesians\">Read Full Paper<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":3722,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[],"class_list":["post-3248","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/3248","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/comments?post=3248"}],"version-history":[{"count":2,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/3248\/revisions"}],"predecessor-version":[{"id":4152,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/3248\/revisions\/4152"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/media\/3722"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/media?parent=3248"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/categories?post=3248"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/tags?post=3248"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}