On the Analysis of CGP Mutation Operators When Inferring Gene Regulatory Networks Using ScRNA-Seq Time Series Data.

Gene Regulatory Networks (GRNs) inference from gene expression data is a hard task and a widely addressed challenge. GRNs can be represented as Boolean models similarly to digital circuits. Cartesian Genetic Programming (CGP), often used for designing circuits, can thus be adopted in the inference of GRNs. The main CGP operator for generating candidate designs is mutation, making its choice important for obtaining good results. Although there are many mutation operators for CGP, to the best of our knowledge, there is no analysis of them in the GRN inference problem. An evaluation of the Single Active Mutation (SAM) and the Semantically-Oriented Mutation Operator (SOMO) is performed here for GRNs inference. Also, a combination of both operators is proposed. We use a benchmark single-cell RNA-Sequencing time series data and its evaluation pipeline to measure the performance of the approaches. The experiments indicate that (i) combining SOMO and SAM provides the best results, and (ii) the results obtained by the proposal are competitive with those from state-of-the-art methods.