Parallel network component analysis technique for gene regulatory network inference

The inference of gene regulatory network has a vital role in understanding the topological order of gene interactions, in addition to how genes are affected by the others genes. One of gene regulatory network techniques is Network Component analysis. The primary drawback of Network Component analysis technique is the intensive computation and time consummation. To avoid these drawbacks, parallel techniques are required. This work presents a parallel technique for gene regulatory network inference; referred as Improved Parallel Computation for Sparse Network Component Analysis (iPSparseNCA) algorithm. To improve the performance of network component analysis technique, iPSparseNCA implements a hybrid parallelism computational model that uses cannon's algorithm for the matrix operations. The performance of iPSparseNCA is measured using different genetic datasets. The computational results indicate that iPSparseNCA achieved high computational speedup, where the achieved speedup reached 1359.85 on 256 processing nodes. These computational results indicate that iPSparseNCA achieved super linear speedup, where, the achieved speedup exceeds the number of used processing nodes. Furthermore, iPSparseNCA has O (N2) time instead of O (M3N2) time for the sequential technique, where M is the number of genes in the dataset and N is the number of the samples. © 2021 John Wiley & Sons Ltd.