Semi-supervised gene shaving method for predicting low variation biological pathways from genome-wide data.

ABSTRACT:

Background

The gene shaving algorithm and many other clustering algorithms identify gene clusters showing high variation across samples. However, gene expression in many signaling pathways show only modest and concordant changes that fail to be identified by these methods. The increasingly available signaling pathway prior knowledge provide new opportunity to solve this problem.

Results

We propose an innovative semi-supervised gene clustering algorithm, where the original gene shaving algorithm was extended and generalized so that prior knowledge of signaling pathways can be incorporated. Different from other methods, our method identifies gene clusters showing concerted and modest expression variation as well as strong expression correlation. Using available pathway gene se

SUBMITTER: Zhu D 

PROVIDER: S-EPMC2648790 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature