Abstract
To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.
Highlights
Protein-protein interaction (PPI) is the core to many fundamental biological processes
Given the sparsity of the known protein-protein interaction (PPI) networks, we believe the coverage of PPIs can be significantly improved if an optimal cutoff can be selected
For evaluation, we applied our algorithm to a yeast core PPI network obtained from [23], which covers 2708 genes with 7123 edges
Summary
Protein-protein interaction (PPI) is the core to many fundamental biological processes. New high-throughput techniques, such as yeast two-hybrid and tandem affinity purification [1], have vastly increased the size of the protein-protein interaction data. With this large amount of protein-protein interaction (PPI) data which is usually. The growing PPI database helps both biological and computational scientists to predict gene functions, functional pathways, protein complexes and improve the diagnosis and treatment of diseases [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. The PPI networks are typically binary (sometimes with limited discrete value) and sparse, partially due to the high false negative rate, which places a hurdle for protein complex prediction
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