Triclustering of Gene Expression Microarray data using Evolutionary Approach

TL;DR

This paper presents a novel evolutionary algorithm-based method for triclustering gene expression data, optimizing a new fitness function to identify high-quality, minimally overlapping triclusters in microarray datasets.

Contribution

It introduces a new fitness function combining 3D MSR and LSL for improved triclustering using evolutionary algorithms.

Findings

EA-based triclustering yields high-quality, low-overlap triclusters.

The method effectively analyzes yeast Saccharomyces gene expression data.

New fitness function enhances tricluster quality and coverage.

Abstract

In Tri-clustering, a sub-matrix is being created, which exhibit highly similar behavior with respect to genes, conditions and time-points. In this technique, genes with same expression values are discovered across some fragment of time points, under certain conditions. In this paper, triclustering using evolutionary algorithm is implemented using a new fitness function consisting of 3D Mean Square residue (MSR) and Least Square approximation (LSL). The primary objective is to find triclusters with minimum overlapping, low MSR, low LSL and covering almost every element of expression matrix, thus minimizing the overall fitness value. To improve the results of algorithm, new fitness function is introduced to find good quality triclusters. It is observed from experiments that, triclustering using EA yielded good quality triclusters. The experiment was implemented on yeast Saccharomyces dataset. Index Terms-Tri-clustering, Genetic Algorithm, Mean squared residue, Volume, Weights, Least square approximation.