Predict the Protein-protein Interaction between Virus and Host through Hybrid Deep Neural Network

Viral infection has been considered as a threat to human health for many years, where protein-protein interactions (PPIs) between viruses and hosts is involved. Researching the PPI between the virus and the host is conducive to understanding the mechanism of virus infection and the development of new drugs. Currently, most of the existing studies based on sequence only focus on extracting sequence features from original amino acid sequences, whereas the redundancy and noise of the features are neglected.In this paper, we employed L1-regularized logistic regression to obtain efficacious sequence features related to PPIs without losing accuracy and generalization. A hybrid deep learning framework which combines convolutional neural network together with a long short term memory network to extract more hidden high-level features was designed to extract more latent features. As it is demonstrated in experiments results, the proposed framework is superior to the current advanced framework in both benchmark data and independent testing and is promising for identifying virus-host interactions.