Gene Network Inference and Expression Prediction Using Recurrent Networks 

ABSTRACT:

We propose the use of recurrent neural networks to perform gene network in- ference and gene expression prediction. Recurrent neural networks are mathematical tools for nonlinear statistical data modeling, and they can represent important tem- poral information that is crucial to the problem of modeling gene regulation. We use a hybrid of particle swarm optimization and differential evolution to overcome the classic obstacle in training recurrent neural networks, which is the tendency to get stuck in local minima during training. We also adapt a previous statistical method for extracting network regulations from a dynamic Bayesian model and apply it to our recurrent neural network model to help discover biological meanings in inferred networks. Success in the modeling of gene regulation and prediction of gene expres- sion will lead to more rapid research and development of effective medicines, earlier diagnosis and treatment of adverse conditions, and vast advancements in the field of biology. Preliminary results on the SOS Repair dataset show striking success in accurate prediction of gene expression levels.