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)
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.