Iterative class discovery and feature selection using Minimal Spanning Trees.

ABSTRACT:

Background

Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples are based on distance metrics utilizing all genes. This has the effect of obscuring clustering in samples that may be evident only when looking at a subset of genes, because noise from irrelevant genes dominates the signal from the relevant genes in the distance calculation.

Results

We describe an algorithm for automatically detecting clusters of samples that are discernable only in a subset of genes. We use iteration between Minimal Spanning Tree based clustering and feature selection to remove noise genes in a step-wise manner while simultaneously sharpening the clustering. Evaluation of this algorit

SUBMITTER: Varma S 

PROVIDER: S-EPMC520744 | biostudies-literature | 2004 Sep

REPOSITORIES: biostudies-literature