Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.

Predicting Missing and Spurious ProteinProtein Interactions Using Graph Embeddings on GO Annotation Graph Proteinprotein interaction (PPI) prediction is a key step towards many bioinformatics applications including prediction of protein functions and drugdisease interactions. However, previous research on PPI prediction rarely considered missing and spurious interactions in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs. Our method leverages the information from both termterm relations among GO terms and termprotein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. We compare our method with methods based on information content and on word embeddings, using three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.