A Novel Anticlustering Filtering Algorithm for the Prediction of Genes as a Drug Target

TL;DR

This paper introduces a new algorithm for analyzing microarray gene expression data to identify differentially expressed genes that could serve as drug targets, avoiding clustering methods.

Contribution

The novel algorithm uses statistical measures to detect differentially expressed genes from time-series data without clustering, validated on yeast data.

Findings

Identified 48 potential drug target genes in yeast.

Algorithm effectively detects genes related to high-temperature resistance.

Validated on large microarray dataset.

Abstract

The high-throughput data generated by microarray experiments provides complete set of genes being expressed in a given cell or in an organism under particular conditions. The analysis of these enormous data has opened a new dimension for the researchers. In this paper we describe a novel algorithm to microarray data analysis focusing on the identification of genes that are differentially expressed in particular internal or external conditions and which could be potential drug targets. The algorithm uses the time-series gene expression data as an input and recognizes genes which are expressed differentially. This algorithm implements standard statistics-based gene functional investigations, such as the log transformation, mean, log-sigmoid function, coefficient of variations, etc. It does not use clustering analysis. The proposed algorithm has been implemented in Perl. The time-series gene expression data on yeast Saccharomyces cerevisiae from the Stanford Microarray Database (SMD)consisting of 6154 genes have been taken for the validation of the algorithm. The developed method extracted 48 genes out of total 6154 genes. These genes are mostly responsible for the yeast's resistants at a high temperature.