{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:16:44Z","timestamp":1774527404358,"version":"3.50.1"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present ASDEC (https:\/\/github.com\/pephco\/ASDEC), a neural-network-based framework that can scan whole genomes for selective sweeps. ASDEC achieves similar classification performance to other convolutional neural network-based classifiers that rely on summary statistics, but it is trained 10\u00d7 faster and classifies genomic regions 5\u00d7 faster by inferring region characteristics from the raw sequence data directly. Deploying ASDEC for genomic scans achieved up to 15.2\u00d7 higher sensitivity, 19.4\u00d7 higher success rates, and 4\u00d7 higher detection accuracy than state-of-the-art methods. We used ASDEC to scan human chromosome 1 of the Yoruba population (1000Genomes project), identifying nine known candidate genes.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad265","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T08:14:34Z","timestamp":1688112874000},"page":"i194-i203","source":"Crossref","is-referenced-by-count":14,"title":["Genome-wide scans for selective sweeps using convolutional neural networks"],"prefix":"10.1093","volume":"39","author":[{"given":"Hanqing","family":"Zhao","sequence":"first","affiliation":[{"name":"Faculty of EEMCS, University of Twente , Enschede, The Netherlands"}]},{"given":"Matthijs","family":"Souilljee","sequence":"additional","affiliation":[{"name":"Faculty of EEMCS, University of Twente , Enschede, The Netherlands"}]},{"given":"Pavlos","family":"Pavlidis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Foundation for Research and Technology-Hellas , Heraklion, Greece"}]},{"given":"Nikolaos","family":"Alachiotis","sequence":"additional","affiliation":[{"name":"Faculty of EEMCS, University of Twente , Enschede, The Netherlands"}]}],"member":"286","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"2023063013474394100_btad265-B1","first-page":"265","author":"Abadi","year":"2016"},{"key":"2023063013474394100_btad265-B2","doi-asserted-by":"crossref","first-page":"e54967","DOI":"10.7554\/eLife.54967","article-title":"A community-maintained standard library of population genetic models","volume":"9","author":"Adrion","year":"2020","journal-title":"Elife"},{"key":"2023063013474394100_btad265-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-018-0085-8","article-title":"Raisd detects positive selection based on multiple signatures of a selective sweep and SNP vectors","volume":"1","author":"Alachiotis","year":"2018","journal-title":"Commun 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