Protein Function Prediction by Random Walks on a Hybrid Graph

Background: Proteins participate in various essential processes of life and hence accurately annotating functional roles of proteins can elucidate the understanding of life and diseases. Objective: Various network-based function prediction models have been proposed to predict protein functions using protein-protein interactions networks, while most of them do not make use of function correlations in functional inference. Furthermore, these models suffer from false positive interactions. Our aim is to solve these problems with advanced machine learning techniques. Method: In this paper, we introduce an approach called protein function prediction by random walks on a hybrid graph (ProHG). ProHG not only takes into account of the function correlation and direct interactions, but also indirect interactions between proteins by functional similarity weight (FS-weight) to alleviate noisy interactions. Results: Experiments on three public accessible PPI networks show that ProHG can take advantage of function correlations and indirect interactions between proteins for function predictions, and it achieves better performance than other related approaches. Conclusion: The extensive empirical study demonstrates that our proposed ProHG is superior to other related methods for function prediction in most cases, and using indirect interactions can boost the performance of network-based function prediction.