Multilevel support vector regression analysis to identify condition-specific regulatory networks.

ABSTRACT:

Motivation

The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification.

Results

We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-specific regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at

SUBMITTER: Chen L 

PROVIDER: S-EPMC2872001 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature