Increasing reliability of protein interactome by combining heterogeneous data sources with weighted network topological metrics

Over the last decade, the development of high-throughput techniques has resulted in a rapid accumulation of protein-protein interaction (PPI) data. However, the high-throughput experimental interaction data is prone to exhibit high level of false-positive and false-negative rates. It is therefore highly desirable to develop an approach to deal with these issues from the computational perspective. Meanwhile, as a variety of genomic and proteomic datasets become available, they provide an opportunity to study the interactions between proteins indirectly. In this paper, we introduce a novel approach that employs the Logistic Regression to integrate heterogeneous types of high-throughput biological data into a weighted biological network. Then, a weighted topological metrics of the network is devised to indicate the interacting possibility of two proteins. We evaluate our method on the Gavin's yeast interaction dataset. The experimental results show that by incorporating heterogeneous data types with weighted network topological metrics, our method improved functional homogeneity and localization coherence compared with existing approaches.