Visualizing multifunctional PPI network with Gene Ontology annotation Proteinprotein interaction (PPI) network data contain interactions between two proteins in organisms. Some proteins are involved in more than one biological process. Those multifunctional proteins are important in the biological system because they connect various biological pathways. However, the multifunctionality of proteins increases the complexity of the PPI network and makes visualizing a challenging problem. In this research, we suggest a new method to visualize the multifunctional PPI network by integrating interaction data and Gene Ontology (GO) terms of each protein. By matching GO annotations of two proteins, we annotate edges between those two proteins with common GO terms. Also, we suggest a novel algorithm for color mapping specialized to express hierarchical relationships among GO terms based on the multidimensional scaling algorithm. Annotated edges are divided corresponding to their GO terms, and visualized by selected color, width, and opacity to represent semantic relationships among edges clearly. Those visual features are assigned to represent semantic distance among GO terms and their concreteness. This visualization method is available through the web.

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.