A neural network based approach for inference and verification of transcriptional regulatory interactions

In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then modeled by analyzing a rat hippocampus development dataset. Finally, we present a thorough computational methodology to test the validity and robustness of the inferred regulations through novel assemblies of relevant testing datasets.