Genome-wide predicting disease-related protein complexes by walking on the heterogeneous network based on data integration and laplacian normalization. (August 2017)

• Record Type: Journal Article
• Authors: Liu, Zhiming; Luo, Jiawei
• Is Part Of: Computational biology and chemistry. Volume 69(2017)
• Page Start: 41
• Page End: 47
• Publication Date: 2017-08
• Subjects: Disease-related protein complex -- Data integration -- Laplacian normalization; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.04.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A novel method of predicting disease-related protein complex is proposed based on the module nature of human genetic disease and the framework of RWR. We combined data integration with laplacian normalization technique to strengthen the weight between seed nodes of the network. Compared with some popular disease-related protein complex identification methods, the experimental results show that our method has reasonable performance. Abstract: Background: Associating protein complexes to human inherited diseases is critical for better understanding of biological processes and functional mechanisms of the disease. Many protein complexes have been identified and functionally annotated by computational and purification methods so far, however, the particular roles they were playing in causing disease have not yet been well determined. Results: In this study, we present a novel method to identify associations between protein complexes and diseases. First, we construct a disease-protein heterogeneous network based on data integration and laplacian normalization. Second, we apply a random walk with restart on heterogeneous network (RWRH) algorithm on this network to quantify the strength of the association between proteins and the query disease. Third, we sum over the scores of member proteins to obtain a summary score for each candidate protein complex, and then rank all candidate protein complexes according to their scores. With a series of leave-one-out cross-validation experiments, we found that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We test our approach with breast cancer and select top 20 highly ranked protein complexes, 17 of the selected protein complexes are evidenced to be connected with breast cancer. Conclusions: Our proposed method is effective in identifying disease-related protein complexes based on data integration and laplacian normalization.