{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:57:20Z","timestamp":1776268640013,"version":"3.50.1"},"reference-count":7,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,3,1]]},"abstract":"<jats:p>Summary: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD\u2019s SVM methodology.<\/jats:p>\n               <jats:p>Availability and implementation: All data and source code are available at https:\/\/cbcl.ics.uci.edu\/public_data\/DANN\/.<\/jats:p>\n               <jats:p>Contact: \u00a0xhx@ics.uci.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu703","type":"journal-article","created":{"date-parts":[[2014,10,23]],"date-time":"2014-10-23T00:08:46Z","timestamp":1414022926000},"page":"761-763","source":"Crossref","is-referenced-by-count":958,"title":["DANN: a deep learning approach for annotating the pathogenicity of genetic variants"],"prefix":"10.1093","volume":"31","author":[{"given":"Daniel","family":"Quang","sequence":"first","affiliation":[{"name":"1 \u00a01Department of Computer Science and 2Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA"}]},{"given":"Yifei","family":"Chen","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Computer Science and 2Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA"}]},{"given":"Xiaohui","family":"Xie","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Computer Science and 2Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA"}]}],"member":"286","published-online":{"date-parts":[[2014,10,22]]},"reference":[{"key":"2023020116164416000_btu703-B1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/491171a","article-title":"One-stop shop for disease genes","volume":"491","author":"Baker","year":"2012","journal-title":"Nature"},{"key":"2023020116164416000_btu703-B2","first-page":"2157","article-title":"Optimized cutting plane algorithm for large-scale risk minimization","volume":"10","author":"Franc","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"2023020116164416000_btu703-B3","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1038\/nature11690","article-title":"Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants","volume":"493","author":"Fu","year":"2013","journal-title":"Nature"},{"key":"2023020116164416000_btu703-B4","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/ng.2892","article-title":"A general framework for estimating the relative pathogenicity of human genetic variants","volume":"46","author":"Kircher","year":"2014","journal-title":"Nat. Genet."},{"key":"2023020116164416000_btu703-B5","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"2023020116164416000_btu703-B6","article-title":"Improving neural networks with dropout","author":"Srivastava","year":"2013"},{"key":"2023020116164416000_btu703-B7","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume-title":"ICML-13","author":"Sutskever","year":"2013"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/5\/761\/49011184\/bioinformatics_31_5_761.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/5\/761\/49011184\/bioinformatics_31_5_761.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:17:33Z","timestamp":1675297053000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/31\/5\/761\/2748191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,10,22]]},"references-count":7,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2015,3,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btu703","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,22]]}}}