Construction of Heterogeneous Biological Networks and Its Application

Abstract

Abstract Background: Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein-protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multi-omics data. ResultsIn this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named PHN (Propagate on Heterogeneous Biological Networks) to score and rank functions in descending order from all functional partners and selected the first L of them as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed six other competing approaches using cross validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the AUROC (Area Under the Receiver-Operating Curve) by no less than 32%.Conclusions:We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multi-omics data and improve the prediction accuracy of protein function by getting rid of the the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.