DKCirc2GO: Predicting Gene Ontology of circRNAs Using Dual KATZ Approach Circular RNAs(circRNAs) have been demonstrated to play significant biological roles in many human biological processes such as competing endogenous RNAs or miRNA sponges, regulating gene transcription, translating proteins and others. Inferring the functions of circRNAs is an important strategy for understanding disease pathogenesis at the molecular level. However, most circRNAs have not been functionally characterized. Developing effective computational approaches to identify potential functions of circRNA has become a hot topic. In this paper, we propose an integrated model, DKCirc2GO, to infer the gene ontology (GO) functions of circRNAs by integrating multiple data sources, including the expression profiles of circRNAs, circRNAprotein associations, proteinprotein interactions (PPI), proteinGO associations, GOGO semantic similarity. The C2P global network is constructed by integrating three heterogeneous networks: circRNAcircRNA similarity network, circRNAprotein association network and proteinprotein interaction network. The P2G global network is constructed by integrating three heterogeneous networks: proteinprotein interaction network, proteinGO association network and GOGO semantic similarity network. The KATZ measure is then employed respectively to calculate similarities in the two global network. The circRNAsproteinGO(cpg) association network is constructed based on the KATZ similarity scores. Finally, we annotate circRNAs with Gene Ontology (GO) terms of their neighboring proteinGO terms. The experimental results show that DKCirc2GO has a significantly better performance than existing stateoftheart methods in terms of Fmax.

DeepGOA: Predicting Gene Ontology Annotations of Proteins via Graph Convolutional Network Gene Ontology (GO) uses a series of standardized and controlled GO terms to describe the molecular functions, biological process roles and cellular locations of gene products (i.e., proteins and RNAs), it structurally organizes GO terms in a direct acyclic graph (DAG). GO has more than 40000 terms and each protein is only annotated with several or dozens of these terms. It is a difficult challenge to accurately annotate relevant GO terms to a protein from such a large number of candidate GO terms. Some deep learning models have been proposed to utilize the GO hierarchy for protein function prediction, but they inadequately utilize GO hierarchy. To use the knowledge encoded in the GO hierarchy, we propose a deep Graph Convolutional Network (GCN) based model (DeepGOA) to predict GO annotations of proteins. DeepGOA firstly utilizes GO annotations and hierarchy to measure the correlations between GO terms and to accordingly update the edge weights of the DAG, and then applies GCN on the updated DAG to learn the semantic representation and latent interrelations of GO terms. At the same time, it uses Convolutional Neural Network (CNN) to learn the feature representation of amino acids sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of two representations, which enables training the whole network endtoend in a coherent fashion. Experiments on two model species (Human and Corn) show that DeepGOA outperforms the stateoftheart deep learning based methods. The ablation study proves that GCN can employ the knowledge of GO and boost the performance.