DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Y | - |
dc.contributor.author | Han, S | - |
dc.contributor.author | Choi, S | - |
dc.contributor.author | Hwang, D | - |
dc.date.accessioned | 2016-03-31T08:14:14Z | - |
dc.date.available | 2016-03-31T08:14:14Z | - |
dc.date.created | 2013-05-29 | - |
dc.date.issued | 2014-03 | - |
dc.identifier.issn | 1467-5463 | - |
dc.identifier.other | 2014-OAK-0000029154 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/14860 | - |
dc.description.abstract | Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, using these experimental methods, it is still challenging to identify temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this review, we summarize concepts and applications of these computational methods for inferring dynamic networks and further summarize methods for estimating spatial transition of biological networks. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | Birmingham, AL | - |
dc.relation.isPartOf | Briefings in Bioinformatics | - |
dc.title | Inference of dynamic networks using time-course data. | - |
dc.type | Article | - |
dc.contributor.college | 정보전자융합공학부 | - |
dc.identifier.doi | 10.1093/BIB/BBT028 | - |
dc.author.google | Kim, Y | - |
dc.author.google | Han, S | - |
dc.author.google | Choi, S | - |
dc.author.google | Hwang, D | - |
dc.relation.volume | 15 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 212 | - |
dc.relation.lastpage | 228 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | Briefings in Bioinformatics | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Briefings in Bioinformatics, v.15, no.2, pp.212 - 228 | - |
dc.identifier.wosid | 000333249500007 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 228 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 212 | - |
dc.citation.title | Briefings in Bioinformatics | - |
dc.citation.volume | 15 | - |
dc.contributor.affiliatedAuthor | Choi, S | - |
dc.contributor.affiliatedAuthor | Hwang, D | - |
dc.identifier.scopusid | 2-s2.0-84896484065 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 25 | - |
dc.description.scptc | 22 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordPlus | GENE REGULATORY NETWORKS | - |
dc.subject.keywordPlus | SUBCELLULAR-LOCALIZATION | - |
dc.subject.keywordPlus | PROTEIN LOCALIZATION | - |
dc.subject.keywordPlus | BAYESIAN NETWORKS | - |
dc.subject.keywordPlus | POSITIVE FEEDBACK | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | TOOL | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | INTERACTOME | - |
dc.subject.keywordPlus | COMPENDIUM | - |
dc.subject.keywordAuthor | dynamic network | - |
dc.subject.keywordAuthor | spatiotemporal dynamics | - |
dc.subject.keywordAuthor | network inference | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
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