Towards a Surrogate-assisted PALLAS algorithm for Gene Regulatory Network Inference - Amorim Neto, Hugo de A.; Loo, Luis; Lacerda, Marcelo G. P. de; Braga Neto, Ulisses; L. Neto, Fernando Buarque de (Anais do Simpósio Brasileiro de Bioinformática (BSB); 2024: Anais do XVII Simpósio Brasileiro de BioinformáticaDO - 10.5753/bsb.2024.245586, 2024)
This paper analyzes the application of surrogate models to improve the efficiency of Gene Regulatory Network (GRN) inference from time-series data. A Radial Basis Function (RBF) surrogate model was integrated with the Penalized mAximum LikeLihood and pArticle Swarms (PALLAS) using a Mixed Fish School Search (MFSS) algorithm to reduce the computational cost associated with evaluating the penalized log-likelihood (PLL) fitness function. Experimental results on the p53-MDM2 negative-feedback loop GRN dataset demonstrate that the surrogate-assisted approach significantly reduced fitness function calls by 50% and 89% while maintaining the quality of the PLL metric, with this showing the potential of surrogate models to accelerate GRN inference.