Gene Selection via Significant Subsets using Silhouette Index

JUAN IGNACIO PASTORE; GUILLERMO ABRAS; DIEGO SEBASTIÁN COMAS; MARCEL BRUN; VIRGINIA BALLARIN

Quilmes Provincia Buenos Aires. Argentina

Congreso; I Congreso Argentino de Bioinformática; 2010

Asociación Argentina de Bioinformática y Biología Computacional. Universidad de Quilmes

Gene selection is an important task in the area of bioinformatics, where significant genes are chosen using some criterion of significance. In the case of classification, like disease vs. normal, tissue type, etc, the criterion used is the ability to provide good features for the classification task. In other cases it is interesting to select large groups of genes with similar behavior, regardless of the class. This task is usually carried on by clustering algorithm, where the whole family of genes, or a subset of them, is grouped into significant clusters. These techniques provide insight on possible co-regulation between genes, but usually provide large, maybe enormous sets, depending on the number of clusters required. In this work we present a new algorithm that provides sets of genes with very similar expression. This is possible by using the complete clustering tree provided by the hierarchical clustering algorithm, and the Silhouette index for ranking of the subsets.