CUDAGRN: Parallel Speedup of Inferring Large Gene Regulatory Networks from Expression Data Using Random Forest

Reverse engineering of the Gene Regulatory Networks (GRNs) from high-throughput gene expression data is one of the most pressing challenges of computational biology. In this paper a method for parallelization of the Gene Regulatory Network inference algorithm, GENIE3, based on GPU by exploiting the compute unified device architecture (CUDA) programming model is designed and implemented. GENIE3 solves regulatory network prediction by developing tree based ensemble of Random forests. Our proposed method significantly improves the computational efficiency of GENIE3 by constructing the forest on the GPU in parallel. Our experiments on real and synthetic datasets show that, CUDA implementation outperforms sequential implementation by achieving a speed-up of 15 times (real data) and 14 to 18 times (synthetic data) respectively.