{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T20:00:09Z","timestamp":1776024009581,"version":"3.50.1"},"reference-count":27,"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":[{"name":"U.S. National Institute of Health","award":["R35GM133678"],"award-info":[{"award-number":["R35GM133678"]}]}],"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>The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>EnHiC is publicly available at https:\/\/github.com\/wmalab\/EnHiC.<\/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\/btab272","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:04:18Z","timestamp":1619136258000},"page":"i272-i279","source":"Crossref","is-referenced-by-count":18,"title":["EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework"],"prefix":"10.1093","volume":"37","author":[{"given":"Yangyang","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , Riverside, CA 92521, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-1621","authenticated-orcid":false,"given":"Wenxiu","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California Riverside , Riverside, CA 92521, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410173628300_btab272-B1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1093\/bioinformatics\/btz540","article-title":"Cooler: scalable storage for hi-c data and other genomically labeled arrays","volume":"36","author":"Abdennur","year":"2020","journal-title":"Bioinformatics"},{"key":"2023062410173628300_btab272-B2","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1101\/gr.160374.113","article-title":"Statistical confidence estimation for hi-c data reveals regulatory chromatin contacts","volume":"24","author":"Ay","year":"2014","journal-title":"Genome Res"},{"key":"2023062410173628300_btab272-B3","article-title":"HiCSR: a Hi-C super-resolution framework for producing highly realistic contact maps","author":"Dimmick","year":"2020","journal-title":"https:\/\/doi.org\/10.1101\/2020.02.24.961714"},{"key":"2023062410173628300_btab272-B4","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1038\/nature11082","article-title":"Topological domains in mammalian genomes identified by analysis of chromatin interactions","volume":"485","author":"Dixon","year":"2012","journal-title":"Nature"},{"key":"2023062410173628300_btab272-B5","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/nature08973","article-title":"A three-dimensional model of the yeast genome","volume":"465","author":"Duan","year":"2010","journal-title":"Nature"},{"key":"2023062410173628300_btab272-B6","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nmeth.4325","article-title":"Comparison of computational methods for hi-c data analysis","volume":"14","author":"Forcato","year":"2017","journal-title":"Nat. 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