DSS: A biclustering method to identify diverse and state specific gene modules in gene expression data
DC Field | Value | Language |
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dc.contributor.author | Kim, J. | - |
dc.contributor.author | Yeu, Y. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Yoon, Y. | - |
dc.contributor.author | Park, S. | - |
dc.date.available | 2020-02-27T20:42:11Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/6638 | - |
dc.description.abstract | The biclustering method is a useful co-clustering technique to identify biologically relevant gene modules. In this paper, we propose a novel method to find not only functionally-related gene modules but also state specific gene modules by applying a genetic algorithm to gene expression data. To identify these gene modules, the proposed method finds biclusters in which genes are statistically overexpressed or under expressed, and are differentially-expressed in the samples in the bicluster compared to the samples not in the bicluster. In addition, we improve the genetic algorithm by adding a selection pool for preserving the diversity of the population. The resulting gene modules exhibit better performances than comparative methods in the GO (Gene Ontology) term enrichment test and an analysis connection between gene modules and disease. This is especially the case with gene modules that receive the highest score in the breast cancer dataset; they are closely linked to the ribosome pathway. Recent studies show that dysregulation of ribosome biogenesis is associated with breast tumor progression. © 2016 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings | - |
dc.subject | Cybernetics | - |
dc.subject | Genes | - |
dc.subject | Genetic algorithms | - |
dc.subject | Macromolecules | - |
dc.subject | Bi-clustering | - |
dc.subject | Breast Cancer | - |
dc.subject | Breast tumor | - |
dc.subject | Co-clustering | - |
dc.subject | Comparative methods | - |
dc.subject | Gene Expression Data | - |
dc.subject | Gene ontology | - |
dc.subject | Ribosome biogenesis | - |
dc.subject | Gene expression | - |
dc.title | DSS: A biclustering method to identify diverse and state specific gene modules in gene expression data | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/SMC.2016.7844279 | - |
dc.identifier.bibliographicCitation | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, pp.430 - 434 | - |
dc.identifier.scopusid | 2-s2.0-85015751455 | - |
dc.citation.endPage | 434 | - |
dc.citation.startPage | 430 | - |
dc.citation.title | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings | - |
dc.contributor.affiliatedAuthor | Yoon, Y. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordPlus | Cybernetics | - |
dc.subject.keywordPlus | Genes | - |
dc.subject.keywordPlus | Genetic algorithms | - |
dc.subject.keywordPlus | Macromolecules | - |
dc.subject.keywordPlus | Bi-clustering | - |
dc.subject.keywordPlus | Breast Cancer | - |
dc.subject.keywordPlus | Breast tumor | - |
dc.subject.keywordPlus | Co-clustering | - |
dc.subject.keywordPlus | Comparative methods | - |
dc.subject.keywordPlus | Gene Expression Data | - |
dc.subject.keywordPlus | Gene ontology | - |
dc.subject.keywordPlus | Ribosome biogenesis | - |
dc.subject.keywordPlus | Gene expression | - |
dc.description.journalRegisteredClass | scopus | - |
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