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A between-class overlapping filter-based method for transcriptome data analysis

Sharma, Alokanand and Imoto, S. and Miyano, S. (2012) A between-class overlapping filter-based method for transcriptome data analysis. Journal of Bioinformatics and Computational Biology, 10 (5). pp. 1-20. ISSN 0219-7200

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Feature selection algorithms play a crucial role in identifying and discovering important genes for cancer classi¯cation. Feature selection algorithms can be broadly categorized into two main groups:¯lter-based methods and wrapper-based methods. Filter-based methods have been quite popular in the literature due to their many advantages, including computational e±ciency, simplistic architecture, and an intuitively simple means of discovering biological and clinical aspects. However, these methods have limitations, and the classi¯cation accuracy of the selected genes is less accurate. In this paper, we propose a set of univariate ¯lter-based methods using a between-class overlapping criterion. The proposed techniques have been compared with many other univariate ¯lter-based methods using an acute leukemia dataset. The following properties have been examined: classi¯-cation accuracy of the selected individual genes and the gene subsets; redundancy check among selected genes using ridge regression and LASSO methods; similarity and sensitivity analyses; functional analysis; and, stability analysis. A comprehensive experiment shows promising results for our proposed techniques. The univariate ¯lter based methods using between-class overlapping criterion are accurate and robust, have biological signi¯cance, and are computationally e±cient and easy to implement. Therefore, they are well suited for biological and clinical discoveries.

Item Type: Journal Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User: Alokanand Sharma
Date Deposited: 12 Aug 2012 23:11
Last Modified: 18 Jan 2017 01:01

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