DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, JK | - |
dc.contributor.author | Choi, S | - |
dc.date.accessioned | 2016-03-31T09:26:29Z | - |
dc.date.available | 2016-03-31T09:26:29Z | - |
dc.date.created | 2011-08-12 | - |
dc.date.issued | 2011-09 | - |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.other | 2011-OAK-0000024045 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/17188 | - |
dc.description.abstract | Methods for discriminative motif discovery in DNA sequences identify transcription factor binding sites (TFBSs), searching only for patterns that differentiate two sets (positive and negative sets) of sequences. On one hand, discriminative methods increase the sensitivity and specificity of motif discovery, compared to generative models. On the other hand, generative models can easily exploit unlabeled sequences to better detect functional motifs when labeled training samples are limited. In this paper, we develop a hybrid generative/discriminative model which enables us to make use of unlabeled sequences in the framework of discriminative motif discovery, leading to semisupervised discriminative motif discovery. Numerical experiments on yeast ChIP-chip data for discovering DNA motifs demonstrate that the best performance is obtained between the purely-generative and the purely-discriminative and the semisupervised learning improves the performance when labeled sequences are limited. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.relation.isPartOf | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | - |
dc.title | Probabilistic Models for Semisupervised Discriminative Motif Discovery in DNA Sequences | - |
dc.type | Article | - |
dc.contributor.college | 정보전자융합공학부 | - |
dc.identifier.doi | 10.1109/TCBB.2010.84 | - |
dc.author.google | Kim, JK | - |
dc.author.google | Choi, S | - |
dc.relation.volume | 8 | - |
dc.relation.issue | 5 | - |
dc.relation.startpage | 1309 | - |
dc.relation.lastpage | 1317 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v.8, no.5, pp.1309 - 1317 | - |
dc.identifier.wosid | 000292681800013 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1317 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1309 | - |
dc.citation.title | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Kim, JK | - |
dc.contributor.affiliatedAuthor | Choi, S | - |
dc.identifier.scopusid | 2-s2.0-79960917573 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 6 | - |
dc.description.scptc | 8 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | BINDING-SITES | - |
dc.subject.keywordPlus | GIBBS | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordAuthor | Graphical models | - |
dc.subject.keywordAuthor | hybrid generative/discriminative models | - |
dc.subject.keywordAuthor | motif discovery | - |
dc.subject.keywordAuthor | probabilistic models | - |
dc.subject.keywordAuthor | semisupervised learning | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
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