A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction
dc.contributor.author | Chen, Peng | |
dc.contributor.author | Hu, ShanShan | |
dc.contributor.author | Zhang, Jun | |
dc.contributor.author | Gao, Xin | |
dc.contributor.author | Li, Jinyan | |
dc.contributor.author | Xia, Junfeng | |
dc.contributor.author | Wang, Bing | |
dc.date.accessioned | 2015-12-21T08:25:16Z | |
dc.date.available | 2015-12-21T08:25:16Z | |
dc.date.issued | 2015-12-03 | |
dc.identifier.citation | A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction 2015:1 IEEE/ACM Transactions on Computational Biology and Bioinformatics | |
dc.identifier.issn | 1545-5963 | |
dc.identifier.doi | 10.1109/TCBB.2015.2505286 | |
dc.identifier.uri | http://hdl.handle.net/10754/584251 | |
dc.description.abstract | Background: 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.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.url | http://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.subject | Dyanmic ensemble system | |
dc.subject | Protein-ligand binding | |
dc.subject | imbalanced samples | |
dc.title | A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction | |
dc.type | Article | |
dc.contributor.department | Computational Bioscience Research Center (CBRC) | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.identifier.journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics | |
dc.eprint.version | Post-print | |
dc.contributor.institution | Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China | |
dc.contributor.institution | College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China | |
dc.contributor.institution | Advanced Analytics Institute, University of Technology, Sydney, New South Wales, Australia. | |
dc.contributor.institution | School of Electronics and Information Engineering, Tongji University, Shanghai 804201, China | |
dc.contributor.affiliation | King Abdullah University of Science and Technology (KAUST) | |
kaust.person | Chen, Peng | |
kaust.person | Gao, Xin | |
refterms.dateFOA | 2018-06-13T12:19:57Z | |
dc.date.published-online | 2015-12-03 | |
dc.date.published-print | 2016-09-01 |
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