{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:07:35Z","timestamp":1776247655242,"version":"3.50.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"NIMH","doi-asserted-by":"publisher","award":["K01MH123896"],"award-info":[{"award-number":["K01MH123896"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01DA051906-01"],"award-info":[{"award-number":["1R01DA051906-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10\u2009bp resolution) using Gradient-weighted Class Activation Mapping.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>DECODE source code and pre-processing scripts are available at decode.gersteinlab.org.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab283","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T16:46:37Z","timestamp":1619455597000},"page":"i280-i288","source":"Crossref","is-referenced-by-count":12,"title":["DECODE: a\n                    <i>De<\/i>\n                    ep-learning framework for\n                    <i>Co<\/i>\n                    n\n                    <i>de<\/i>\n                    nsing enhancers and refining boundaries with large-scale functional assays"],"prefix":"10.1093","volume":"37","author":[{"given":"Zhanlin","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Statistics & Data Science, Yale University , New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California , Irvine, CA 92617, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California , Irvine, CA 92617, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Genetics and Genomic Sciences, The Icahn School of Medicine at Mount Sinai , New York, NY 10029-6574, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin Renqiang","family":"Min","sequence":"additional","affiliation":[{"name":"NEC Laboratories America , Princeton, NJ 08540, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Gerstein","sequence":"additional","affiliation":[{"name":"Department of Statistics & Data Science, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Computer Science, Yale University , New Haven, CT 06520, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410181728300_btab283-B1","doi-asserted-by":"crossref","first-page":"i313","DOI":"10.1093\/bioinformatics\/btp191","article-title":"Toward a gold standard for promoter prediction evaluation","volume":"25","author":"Abeel","year":"2009","journal-title":"Bioinformatics"},{"key":"2023062410181728300_btab283-B2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. 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