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

Abstract Motivation 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. Results In this article, we present a novel global pooling method: expectation pooling for predicting DNA–protein binding. Our pooling method stems naturally from the expectation maximization algorithm, and its benefits can be interpreted both statistically and via deep learning theory. Through experiments, we demonstrate that our pooling method improves the prediction performance DNA–protein binding. Our interpretable pooling method combines probabilistic ideas with global pooling by taking the expectations of inputs without increasing the number of parameters. We also analyze the hyperparameters in our method and propose optional structures to help fit different datasets. We explore how to effectively utilize these novel pooling methods and show that combining statistical methods with deep learning is highly beneficial, which is promising and meaningful for future studies in this field. Availability and implementation All code is public in https://github.com/gao-lab/ePooling. Supplementary information Supplementary data are available at Bioinformatics online.