Abstract
With the increasing availability of high-throughput data, various computational methods have recently been developed for understanding the cell through protein-protein interaction (PPI) networks at a systems level. However, due to the incompleteness of the original PPI networks those efforts have been significantly hindered. In this paper, we propose a two stage method to predict underlying links between two originally unlinked protein pairs. First, we measure gene expression and gene functional similarly between unlinked protein pairs on Saccharomyces cerevisiae benchmark network and obtain new constructed networks. Then, we select the significant part of the new predicted links by analyzing the difference between essential proteins that have been identified based on the new constructed networks and the original network. Furthermore, we validate the performance of the new method by using the reliable and comprehensive PPI dataset obtained from the STRING database and compare the new proposed method with four other random walk-based methods. Comparing the results indicates that the new proposed strategy performs well in predicting underlying links. This study provides a general paradigm for predicting new interactions between protein pairs and offers new insights into identifying essential proteins.
Highlights
With the rapid development of modern high-throughput technologies such as yeast twohybrid (Y2H)screens [1, 2], tandem affinity purification (TAP) [3], and mass spectrometric protein complex identification (MS-PCI) [4], large scale protein-protein interaction (PPI) data are available for many organisms
PPI networks are widely used for predicting protein complexes or functional modules [6, 7], as well as essential proteins [8, 9] and proteins associated with a certain complex disease [10]
The growing size and complexity of experimental data obtained from high throughput technologies are incomplete, and the PPI network we obtained through highthroughput technology is far from complete; only a fraction of true PPIs have been documented even for well-known species [11]
Summary
With the rapid development of modern high-throughput technologies such as yeast twohybrid (Y2H)screens [1, 2], tandem affinity purification (TAP) [3], and mass spectrometric protein complex identification (MS-PCI) [4], large scale PPI data are available for many organisms. PPI networks are widely used for predicting protein complexes or functional modules [6, 7], as well as essential proteins [8, 9] and proteins associated with a certain complex disease [10]. A novel method for predicting interactions underlying PPI networks. The growing size and complexity of experimental data obtained from high throughput technologies are incomplete, and the PPI network we obtained through highthroughput technology is far from complete; only a fraction of true PPIs have been documented even for well-known species [11]. The incompleteness of the PPI network will severely impair the prediction precision. Revealing the unknown part of these networks by biological experimental methods is time-consuming and expensive
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