Predicting protein functions through nonnegative matrix factorization regularized by proteinprotein interaction network and gene functional information Protein function prediction is necessary for understanding life and is valuable for application on drug design and health care. It is still a big challenge to predict protein function correctly by integrating multibiological information. In this work, we propose a novel nonNegative Matrix Factorization (NMF) based method regularized by PPI network and GO similarity network, namely PONMF, for protein function prediction, which decomposes the known GOprotein association matrix into two lowrank matrixes for proteins and GO terms. In the course of factorization, PONMF incorporates the label matrix factorization term, an additional network regularization term and a GO similarity regularization term into the objective function. Finally, the potential protein functions are predicted by referring to the product of the two lowrank matrixes. PONMF not only successfully integrates diverse biological information to predict protein functions, but also naturally partitions proteins into different modules and infers functions from the proteins in the same modules. Our methods as well as the other two stateoftheart methods (UBiRW and NMFGO) are applied to predict functions for protein of S. cerevisiae and H. sapiens. The prediction results show that PONMF outperforms the other two existing methods.

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.