Multi-objective Optimization Approach to find Biclusters in Gene Expression Data
Abstract
Gene expression levels of organisms are measured by DNA microarrays. Finding biclusters in gene expression matrices provides invaluable information about effects of disease at the genetic level. These biclusters could identify which genes are up-regulated/down-regulated under certain conditions. This paper investigates a methodology for evolutionary-based biclustering using the NSGA-II algorithm. It also presents an improvement to the recovery and relevance external validation metrics as well as a new method for synthetic data generation for biclustering. Results obtained demonstrate its effectiveness in discovering useful biclusters on varied synthetic data when applied with the average Spearman's rho measure as the fitness function.
Department(s)
Engineering Program
Document Type
Conference Proceeding
DOI
https://doi.org/10.1109/CIBCB.2019.8791451
Keywords
biclustering, evolutionary algorithm, gene expression data
Publication Date
7-1-2019
Recommended Citation
Dale, Jeffrey, Junya Zhao, and Tayo Obafemi-Ajayi. "Multi-objective Optimization Approach to find Biclusters in Gene Expression Data." In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-8. IEEE, 2019.
