An integration of deep learning with feature embedding for protein-protein interaction prediction.

ABSTRACT: Protein-protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein-protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein-protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Comb

SUBMITTER: Yao Y 

PROVIDER: S-EPMC6585896 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature