This paper presents a parallel algorithm for gene regulatory network construction, hereby referred to as H2pcGRN. The construction of gene regulatory network is a vital methodology for investigating the genes interactions' topological order, annotating the genes functionality and demonstrating the regulatory process. One of the approaches for gene regulatory network construction techniques is based on the component analysis method. The main drawbacks of component analysis-based algorithms are its intensive computations that consume time. Despite these drawbacks, this approach is widely applied to infer the regulatory network. Therefore, introducing parallel techniques is indispensable for gene regulatory network inference algorithms. H2pcGRN is a hybrid high performance-computing algorithm for gene regulatory network inference. The proposed algorithm is based on both the hybrid parallelism architecture and the generalized cannon's algorithm. A variety of gene datasets is used for H2pcGRN assessment and evaluation. The experimental results indicated that H2pcGRN achieved super-linear speedup, where its computational speedup reached 570 on 256 processing nodes.
Nonnegative Network Component Analysis by Linear Programming for Gene Regulatory Network Reconstruction
