Multi-level Support Vector Regression analysis to identify condition-specific regulatory networks

ABSTRACT: 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. We present a new approach, namely multi-level support vector regression (ml-SVR), to systematically identify conditionspecific regulatory modules. The approach is built upon a multi-level 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 finer levels. At each level, a

ORGANISM(S): Homo sapiens

SUBMITTER: Yue Wang 

PROVIDER: E-GEOD-20700 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress