Parallelization of a Method to Infer Genetic Networks in Multi-CPU Systems

Genetic regulatory networks represent the interactions present between genes and are a crucial step in understanding cellular physiology and complex pathological phenotypes. There are various methods to infer these networks from a set of genetic data. Among them, the MRNET method stands out, which is based on the mRMR feature selection algorithm. However, constructing genetic regulatory networks with MRNET is a computationally expensive process for large-scale datasets due to its cubic complexity concerning the number of genes. The objective of this work is to develop parallel versions of MRNET that can be executed on shared-memory parallel systems to accelerate its execution.