Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm

An improved poor and rich optimization algorithm

The poor and rich optimization algorithm (PRO) is a new bio-inspired meta-heuristic algorithm based on the behavior of the poor and the rich. PRO suffers from low convergence speed and premature convergence, and easily traps in the local optimum, when solving very complex function optimization problems. To overcome these limitations, this study proposes an improved poor and rich optimization (IPRO) algorithm. First, to meet the requirements of convergence speed and swarm diversity requirements across different evolutionary stages of the algorithm, the population is dynamically divided into the poor and rich sub-population. Second, for the rich sub-population, this study designs a novel individual updating mechanism that learns from the evolution information of the global optimum individual and that of the poor sub-population simultaneously, to further accelerate convergence speed and minimize swarm diversity loss. Third, for the poor sub-population, this study designs a novel individual updating mechanism that improves some evolution information by learning alternately from the rich and Gauss distribution, gradually improves evolutionary genes, and maintains swarm diversity. The IPRO is then compared with four state-of-the-art swarm evolutionary algorithms with various characteristics on the CEC 2013 test suite. Experimental results demonstrate the competitive advantages of IPRO in convergence precision and speed when solving function optimization problems.

Modified Half-System Based Method for Reverse Engineering of Gene Regulatory Networks

The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system. Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3. The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases.