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Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization

Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization
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Author(s): Prativa Agarwalla (Heritage Institute of Technology, India)and Sumitra Mukhopadhyay (Institute of Radiophysics and Electronics, India)
Copyright: 2018
Pages: 27
Source title: Applying Big Data Analytics in Bioinformatics and Medicine
Source Author(s)/Editor(s): Miltiadis D. Lytras (Deree - The American College of Greece, Greece)and Paraskevi Papadopoulou (Deree - The American College of Greece, Greece)
DOI: 10.4018/978-1-5225-2607-0.ch014

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Abstract

Pathway information for cancer detection helps to find co-regulated gene groups whose collective expression is strongly associated with cancer development. In this paper, a collaborative multi-swarm binary particle swarm optimization (MS-BPSO) based gene selection technique is proposed that outperforms to identify the pathway marker genes. We have compared our proposed method with various statistical and pathway based gene selection techniques for different popular cancer datasets as well as a detailed comparative study is illustrated using different meta-heuristic algorithms like binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE), binary coded artificial bee colony (BABC) and genetic algorithm (GA). Experimental results show that the proposed MS-BPSO based method performs significantly better and the improved multi swarm concept generates a good subset of pathway markers which provides more effective insight to the gene-disease association with high accuracy and reliability.

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