Integrative random forest for gene regulatory network inference.

ABSTRACT:

Motivation

Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference. Towards this goal, we propose a novel algorithm named iRafNet: integrative random forest for gene regulatory network inference.

Results

iRafNet is a flexible, unified integrative framework that allows information from heterogeneous data, such as protein-protein interactions, transcription factor (TF)-DNA-binding, gene knock-down, to be jointly considered for GRN inference. Using test data from the DREAM

SUBMITTER: Petralia F 

PROVIDER: S-EPMC4542785 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature