Approaches that integrate prior biological knowledge into the classification and clustering tasks are promising alternatives to data-driven models and have gained much attention in recent years. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of response in clinical trials. We incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms as well as single gene shrinkage into the classification model.