BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research.

ABSTRACT: Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model-based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can inc

SUBMITTER: Iglesias-Martinez LF 

PROVIDER: S-EPMC5120305 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature