Using a grid computing-based meta-evolutionary mining approach for the microarray data cancer-categorization. Issue 1 (6th March 2017)

• Record Type: Journal Article
• Authors: Chiang, Tai-Wei; Chen, Ta-Cheng
• Is Part Of: Engineering computations. Volume 34:Issue 1(2017)
• Page Start: 134
• Page End: 144
• Publication Date: 2017-03-06
• Subjects: Cancer categorization -- Grid computing -- Meta-evolutionary approach -- Microarray data; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-11-2015-0355
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The categorization response model through gene expression patterns turns into one of the most favorable utilizations of the microarray technology. In this study, the aim is to propose a grid computing-based meta-evolutionary mining approach as a categorization response model for gene selection and cancer classification. Design/methodology/approach: The proposed approach is based on the grid computing infrastructure for establishing the best attributes set selected from a big microarray data. The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy. Findings: Examples for several benchmarking cancer microarray data sets were used to evaluate the proposed approach, whose results are also compared with other approaches in literatures. Experimental results from four benchmarking problems indicate that the proposed approach works effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures. Originality/value: The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach to discover the best feature subset automatically from the microarray tumor database. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.