Microarray Gene Expression Data Mining using High End Clustering Algorithm based on Attraction-Repulsion Technique

摘要

Microarray Gene expression data analysis is one of the key domains in the modern cellular and molecular biology system design and analysis; shortly we called it computational simulation of genome-wide expression from DNA hybridization. We present here a high end clustering algorithm basically a technique following the inspiration led by natural attraction and the repulsion processes. It groups the similarly expressed genes in same clusters, co-expressed and differently expressed ones in different clusters. Most importantly, it takes into account of the outliers in an efficient manner by not allowing them to interfere with the similarly expressed gene clusters on the fly. In the first clustering process, it calculates the distances of all the genes in a proximity range set in prior, henceforth attracting all the least distant genes from the seed gene. Varying the proximity range in the subsequent run, repulse the maximally distant genes from the same cluster, thereby achieving a near to perfect cluster formation at the end. We include cluster validity testing using Hubert?s statistics technique, which shows a very optimal clusters validity result.