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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying

Sunil Kumar Prabhakar¹, Harikumar Rajaguru², Sun-Hee Kim¹

¹Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea.

Biomed Research International

|October 26, 2020

Summary

This study introduces a novel bilevel feature selection approach using Multivariate Minimum Redundancy-Maximum Relevance (MRMR) and optimization techniques to improve colon cancer detection from DNA microarray data, achieving 99.16% accuracy.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Colon cancer is a leading cause of death, often starting as benign growths due to somatic alterations affecting gene expression.
DNA microarray technology enables assessment of gene expression levels, distinguishing tumors by expression patterns.
High-dimensional microarray data presents challenges due to the curse of dimensionality.

Purpose of the Study:

To propose a bilevel feature selection method for analyzing high-dimensional DNA microarray data in colon cancer research.
To reduce dimensionality and identify optimal gene features for accurate colon cancer classification.

Main Methods:

A two-level feature selection approach was employed.
Level 1: Dimensionality reduction using Multivariate Minimum Redundancy-Maximum Relevance (MRMR).
Level 2: Six optimization techniques (IWO, TLBO, LCO, BASO, CSO, FFO) for feature selection, followed by classification with five classifiers.

Main Results:

The combination of MRMR with Invasive Weed Optimization (IWO) followed by Quadratic Discriminant Analysis (QDA) yielded the highest classification accuracy.
An optimal classification accuracy of 99.16% was achieved using this integrated approach.

Conclusions:

The proposed bilevel feature selection method effectively addresses the curse of dimensionality in microarray data for colon cancer.
The MRMR-IWO-QDA pipeline demonstrates high potential for accurate and efficient colon cancer diagnosis.