Multi-objective Optimization Approach To Find Biclusters In Gene Expression Data

Jeffrey Dale; Junya Zhao; Tayo Obafemi-Ajayi

doi:10.1109/CIBCB.2019.8791451

Multi-objective Optimization Approach To Find Biclusters In Gene Expression Data

Jeffrey Dale
, Junya Zhao
, Tayo Obafemi-Ajayi

Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
DOIs	https://doi.org/10.1109/CIBCB.2019.8791451
State	Published - Jul 1 2019

Keywords

biclustering
evolutionary algorithm
gene expression data

Disciplines

Electrical and Computer Engineering

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Multi-objective Optimization Approach To Find Biclusters In GeneFinal published version, 971 KBLicense: Unspecified

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title = "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.",

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Y1 - 2019/7/1

N2 - 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.

AB - 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.

KW - biclustering

KW - evolutionary algorithm

KW - gene expression data

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/4822

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DO - 10.1109/CIBCB.2019.8791451

M3 - Article

JO - 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019

JF - 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019

ER -

Multi-objective Optimization Approach To Find Biclusters In Gene Expression Data

Abstract

Keywords

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