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dc.contributor.authorChen, Peng
dc.contributor.authorHu, ShanShan
dc.contributor.authorZhang, Jun
dc.contributor.authorGao, Xin
dc.contributor.authorLi, Jinyan
dc.contributor.authorXia, Junfeng
dc.contributor.authorWang, Bing
dc.date.accessioned2015-12-21T08:25:16Z
dc.date.available2015-12-21T08:25:16Z
dc.date.issued2015-12-03
dc.identifier.citationA sequence-based dynamic ensemble learning system for protein ligand-binding site prediction 2015:1 IEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.identifier.issn1545-5963
dc.identifier.doi10.1109/TCBB.2015.2505286
dc.identifier.urihttp://hdl.handle.net/10754/584251
dc.description.abstractBackground: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7346422
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectDyanmic ensemble system
dc.subjectProtein-ligand binding
dc.subjectimbalanced samples
dc.titleA sequence-based dynamic ensemble learning system for protein ligand-binding site prediction
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Health Sciences, Anhui University, Hefei, Anhui 230601, China
dc.contributor.institutionCollege of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China
dc.contributor.institutionAdvanced Analytics Institute, University of Technology, Sydney, New South Wales, Australia.
dc.contributor.institutionSchool of Electronics and Information Engineering, Tongji University, Shanghai 804201, China
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personChen, Peng
kaust.personGao, Xin
refterms.dateFOA2018-06-13T12:19:57Z
dc.date.published-online2015-12-03
dc.date.published-print2016-09-01


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