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.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 430-434 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509018970 |
| DOIs | |
| Publication status | Published - 2017 Feb 6 |
| Event | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: 2016 Oct 9 → 2016 Oct 12 |
Publication series
| Name | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings |
|---|
Other
| Other | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 |
|---|---|
| Country | Hungary |
| City | Budapest |
| Period | 16/10/9 → 16/10/12 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Control and Optimization
- Human-Computer Interaction
Cite this
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DSS : A biclustering method to identify diverse and state specific gene modules in gene expression data. / Kim, Jungrim; Yeu, Yunku; Kim, Jeongwoo; Yoon, Youngmi; Park, Sang Hyun.
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 430-434 7844279 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - DSS
T2 - A biclustering method to identify diverse and state specific gene modules in gene expression data
AU - Kim, Jungrim
AU - Yeu, Yunku
AU - Kim, Jeongwoo
AU - Yoon, Youngmi
AU - Park, Sang Hyun
PY - 2017/2/6
Y1 - 2017/2/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015751455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015751455&partnerID=8YFLogxK
U2 - 10.1109/SMC.2016.7844279
DO - 10.1109/SMC.2016.7844279
M3 - Conference contribution
T3 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
SP - 430
EP - 434
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -