{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:15:12Z","timestamp":1771298112740,"version":"3.50.1"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T00:00:00Z","timestamp":1547424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM126002"],"award-info":[{"award-number":["R01GM126002"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01HL116720"],"award-info":[{"award-number":["R01HL116720"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM113250"],"award-info":[{"award-number":["R01GM113250"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01HL105397"],"award-info":[{"award-number":["R01HL105397"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016204","name":"Minnesota Supercomputing Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100016204","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Enhancer\u2013promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Computer code is available at https:\/\/github.com\/zzUMN\/Combine-CNN-Enhancer-and-Promoters.<\/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\/bty1050","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:23:14Z","timestamp":1547180594000},"page":"2899-2906","source":"Crossref","is-referenced-by-count":76,"title":["A simple convolutional neural network for prediction of enhancer\u2013promoter interactions with DNA sequence data"],"prefix":"10.1093","volume":"35","author":[{"given":"Zhong","family":"Zhuang","sequence":"first","affiliation":[{"name":"University of Minnesota Department of Electrical and Computer Engineering, , Minneapolis, MN, USA"}]},{"given":"Xiaotong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Statistics, University of Minnesota , Minneapolis, MN, USA"}]},{"given":"Wei","family":"Pan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, University of Minnesota , Minneapolis, MN, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,1,14]]},"reference":[{"key":"2023062711300117200_bty1050-B1","doi-asserted-by":"crossref","first-page":"878.","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. 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