Inferring cancer subnetwork markers using density-constrained biclustering.

ABSTRACT:

Motivation

Recent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer.

Results

We explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predi

SUBMITTER: Dao P 

PROVIDER: S-EPMC2935415 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature