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Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data

Author : Anirban Mukhopadhyay

Published in: Multi-Objective Optimization

Publisher: Springer Singapore

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Abstract

The advances in microarray technology have allowed the biologists to simultaneously study the levels of expression of a large set of genes over different time points. Clustering algorithms are used to discover groups of genes that are similarly expressed over all the experimental conditions. In this chapter, an approach for combining experimental gene expression information and biological information in the form of Gene Ontology (GO) knowledge through multiobjective clustering has been presented. The proposed method combines the expression-based and GO-based measures to compute the distances between the genes. Moreover, it simultaneously optimizes two objective functions, one from gene expression point of view and another from GO point of view. The performance of the proposed technique has been demonstrated on real-life gene expression dataset of yeast cell cycle. Moreover, biological relevance studies have been conducted for the produced clusters to demonstrate the effectiveness of the proposed technique.

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Metadata
Title
Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data
Author
Anirban Mukhopadhyay
Copyright Year
2018
Publisher
Springer Singapore
Book
Multi-Objective Optimization

Print ISBN: 978-981-13-1470-4

Electronic ISBN: 978-981-13-1471-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-1471-1_3

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