Mutual information estimation reveals global associations between stimuli and biological processes.

ABSTRACT:

Background

Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions.

Results

To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiv

SUBMITTER: Suzuki T 

PROVIDER: S-EPMC2648797 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature