Gene regulatory network reconstruction based on Optimized Particle Swarm Optimization with differential equation

A novel inferring network algorithm is proposed in this paper, which can be used to infer gene regulatory network. One of the contributions of this paper is a novel and effective optimal algorithm to search the best value of given function. Furthermore, the optimal algorithm can be used to find the best suitable gene regulatory network of given genes. Mostly, the singular value decomposition was used to reduce the scope of the solution of given problem. This method can avoid local convergence in the optimal algorithm mostly. The simulated data and real data which is Yeast Saccharomyces cerevisiae cell cycle gene expression profiles from the Spellman's data were used to test the efficiency of the proposed algorithm. And we compared our algorithm with traditional genetic algorithm and particle swarm optimization algorithm. the efficiency of the algorithm is demonstrated in this paper.