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dc.contributor.authorCao, Fanen_US
dc.contributor.authorZhang, Yuen_US
dc.contributor.authorCai, Yichaoen_US
dc.contributor.authorAnimesh, Sambhavien_US
dc.contributor.authorZhang, Yingen_US
dc.contributor.authorAkincilar, Semih Canen_US
dc.contributor.authorLoh, Yan Pingen_US
dc.contributor.authorLi, Xinyaen_US
dc.contributor.authorChng, Wee Jooen_US
dc.contributor.authorTergaonkar, Vinayen_US
dc.contributor.authorKwoh, Chee Keongen_US
dc.contributor.authorFullwood, Melissa Janeen_US
dc.identifier.citationCao, F., Zhang, Y., Cai, Y., Animesh, S., Zhang, Y., Akincilar, S. C., Loh, Y. P., Li, X., Chng, W. J., Tergaonkar, V., Kwoh, C. K. & Fullwood, M. J. (2021). Chromatin interaction neural network (ChINN) : a machine learning-based method for predicting chromatin interactions from DNA sequences. Genome Biology, 22, 226-.
dc.description.abstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofGenome Biologyen_US
dc.rights© The Author(s) 2021 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.en_US
dc.subjectScience::Biological sciencesen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleChromatin interaction neural network (ChINN) : a machine learning-based method for predicting chromatin interactions from DNA sequencesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.organizationInstitute of Molecular and Cell Biology, A*STARen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywords3D Genome Organizationen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation (NRF) Singapore through an NRF Fellowship awarded to M.J.F (NRF-NRFF2012-054) and NTU start-up funds awarded to M.J.F. This research is supported by the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 awarded to Daniel Tenen as lead PI with M.J.F as co-investigator (MOE2014-T3-1-006). This research is supported by a National Research Foundation Competitive Research Programme grant awarded to V.T. as lead PI and M.J.F. as co-PI (NRF-CRP17-2017-02). This research is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative. This research is supported by a Ministry of Education Tier II grant awarded to M.J.F (T2EP30120-0020).en_US
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