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dc.contributor.authorWong, Ka-Chun
dc.contributor.authorLi, Yue
dc.contributor.authorPeng, Chengbin
dc.contributor.authorWong, Hau-San
dc.date.accessioned2015-12-21T08:26:26Z
dc.date.available2015-12-21T08:26:26Z
dc.date.issued2015-06-11
dc.identifier.citationA Comparison Study for DNA Motif Modeling on Protein Binding Microarray 2015:1 IEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.identifier.issn1545-5963
dc.identifier.doi10.1109/TCBB.2015.2443782
dc.identifier.urihttp://hdl.handle.net/10754/584252
dc.description.abstractTranscription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7122289
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.subjectCrowding
dc.subjectGenetic Algorithm
dc.subjectProtein Binding Microarray
dc.subjectRanking
dc.subjectTranscription Factor Binding Site
dc.titleA Comparison Study for DNA Motif Modeling on Protein Binding Microarray
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.identifier.journalIEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Hong Kong
dc.contributor.institutionComputer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personPeng, Chengbin
refterms.dateFOA2018-06-13T12:22:14Z
dc.date.published-online2015-06-11
dc.date.published-print2016-03-01


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