A between-Class Overlapping Filter-Based Method for transcriptome Data Analysis

Feature selection algorithms play a crucial role in identifying and discovering important genes for cancer classification. Feature selection algorithms can be broadly categorized into two main groups: filter-based methods and wrapper-based methods. Filter-based methods have been quite popular in the literature due to their many advantages, including computational efficiency, simplistic architecture, and an intuitively simple means of discovering biological and clinical aspects. However, these methods have limitations, and the classification accuracy of the selected genes is less accurate. In this paper, we propose a set of univariate filter-based methods using a between-class overlapping criterion. The proposed techniques have been compared with many other univariate filter-based methods using an acute leukemia dataset. The following properties have been examined: classification 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 filter based methods using between-class overlapping criterion are accurate and robust, have biological significance, and are computationally efficient and easy to implement. Therefore, they are well suited for biological and clinical discoveries.

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