Inferring network interactions using recurrent neural networks and swarm intelligence.

TLDR: The results indicate that the proposed hybrid SI-RNN algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data.

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 I INTRODUCTION inference algorithm based on GAs for the optimization of the influence matrix of gene regulatory network In (13), GAs and ANNs are combined to determine gene interactions in temporal gene expression data In this paper, we propose to apply a hybrid of ANNs and swarm intelligence (SI) methods (12) to infer network interactions from time-series data The architecture and the synaptic weights of a recurrent neural network (RNN) are optimized using ant colony optimization (ACO) and particle swarm optimization (PSO) methods, respectively Unlike previous computational methods, which targeted at one-step- ahead prediction of time-series data (13), our method enables a multi-step-ahead prediction This is achieved through our RNN, which is self-evolutionary The RNN starts with a given initial condition, evolves, and eventually reaches final states The proposed hybrid SI-RNN algorithm selects the architecture of the RNN and weights not only to mimic the response of the unknown network at each time point but also to identify the structure of the network that generated the time-series data This is a challenging task given that there may be many possible structures with responses that closely match the generated data The algorithm evaluates various structures through the cross- validation method to avoid the selection of a wrong structure and to make sure that the correct structure is identified despite the presence of noise and complexity of the unknown network We successfully applied the algorithm to infer simulated network interactions