Detecting protein complex based on hierarchical compressing network embedding Detecting protein complexes from proteinprotein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining highdensity regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPCHCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPCHCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPCHCNE outperforms those stateoftheart complex detection methods.

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.