Expectation pooling: an effective and interpretable pooling method for predicting DNA-protein binding

Xiao Luo, Xinming Tu, Yang Ding, Ge Gao, Minghua Deng

Abstract: Convolutional neural networks (CNNs) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. While previous studies have built a connection between CNNs and probabilistic models, simple models of CNNs cannot achieve sufficient accuracy on this problem. Recently, some methods of neural networks have increased performance using complex neural networks whose results cannot be directly interpreted. However, it is difficult to combine probabilistic models and CNNs effectively to improve DNA–protein binding predictions.