Multi-objective Simulated Annealing Variants to Infer Gene Regulatory Network: A Comparative Study

Gene Regulatory Network (GRN) is formed due to mutual transcriptional regulation within a set of protein coding genes in cellular context of an organism. Computational inference of GRN is important to understand the behavior of each gene in terms of change in its protein production rate (expression level). As Recurrent Neural Network (RNN) is efficient in GRN modeling, a bi-objective RNN formulation has been applied here. Based on Archived Multi Objective Simulated Annealing (AMOSA), four algorithms, namely, AMOSA Revised (AMOSAR), Modified Freezing based AMOSA (AMOFSA), Tabu based AMOSA (AMOTSA) and Modified Freezing and Tabu based AMOSA (AMOFTSA) have been proposed and applied to RNN (treated as GRN) for parameter learning taking four gene expression time series datasets. Comparative studies on the performance of the algorithms (based on each dataset) have been made in terms of the number of GRNs obtained in the final non-dominated front and the performance metrics, namely, recall, precision and f1 score. Two proposed variants, namely, AMOFSA and AMOTSA have been found competitive in performance. Experimental observations and statistical analysis show that, modified algorithms are better than AMOSAR and the state-of-the-art algorithms in respect of the above-mentioned metrics.

Automated summary

In this study, four algorithms based on the Archived Multi Objective Simulated Annealing (AMOSA) framework were proposed for parameter learning in Recurrent Neural Network (RNN) modeling of Gene Regulatory Network (GRN). Comparative studies on performance metrics, including recall, precision and f1 score, showed that the modified algorithms, AMOFSA and AMOTSA, outperformed AMOSAR and other state-of-the-art algorithms in terms of the number of GRNs obtained in the final non-dominated front.