title: Inferring network interactions using recurrent neural networks and swarm intelligence.
abstract: We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN that mimics the true structure of an unknown network and the time-series data that the network generated. We applied the proposed hybrid SI-RNN algorithm to infer a simulated genetic network. The results indicate that the algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data.
authors.affiliation: Dept. of Biostat., Bioinf., & Biomath., Georgetown Univ., Washington, DC 20057, USA.
authors.forename: Habtom W
authors.initials: HW
authors.lastname: Ressom
authors.forename: Yuji
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
mesh_terms: D000465:Algorithms; D001695:Biology / Q000379:methods / Q000639:trends; D001709:Biotechnology / Q000379:methods / Q000639:trends; D003198:Computer Simulation; D005786:Gene Expression Regulation; D005823:Genetics / Q000639:trends*; D008957:Models, Genetic; D009415:Nerve Net