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dc.contributor.authorWong, Ka-Chun
dc.contributor.authorLi, Yue
dc.contributor.authorPeng, Chengbin
dc.contributor.authorMoses, Alan M.
dc.contributor.authorZhang, Zhaolei
dc.date.accessioned2016-01-04T15:11:16Z
dc.date.available2016-01-04T15:11:16Z
dc.date.issued2015-11
dc.date.submitted2015-10
dc.identifier.issn0305-1048
dc.identifier.issn1362-4962
dc.identifier.urihttp://hdl.handle.net/1721.1/100577
dc.description.abstractThe protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.en_US
dc.language.isoen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/nar/gkv1134en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en_US
dc.sourceOxford University Pressen_US
dc.titleComputational learning on specificity-determining residue-nucleotide interactionsen_US
dc.typeArticleen_US
dc.identifier.citationWong, Ka-Chun, Yue Li, Chengbin Peng, Alan M. Moses, and Zhaolei Zhang. “Computational Learning on Specificity-Determining Residue-Nucleotide Interactions.” Nucleic Acids Research (November 2, 2015): gkv1134.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.mitauthorLi, Yueen_US
dc.relation.journalNucleic Acids Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsWong, Ka-Chun; Li, Yue; Peng, Chengbin; Moses, Alan M.; Zhang, Zhaoleien_US
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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