Abstract
Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.
Highlights
In the proteomics era, various high throughput experimental techniques and computational methods have produced enormous protein interactions data [1], which have contributed to predict protein function [2,3] and detect protein complexes from protein–protein interaction (PPI) networks [4].Prediction of protein complexes can help to understand principles of cellular organization and biological functions of proteins [5,6,7]
We address the above limits and propose a new seed-expansion graph clustering algorithm (SEGC) that produces overlapped clusters for protein complex detection
The results show that SEGC outperforms other algorithms under full coverage in terms of both F-measure and accuracy with a real benchmark protein complex data set
Summary
Various high throughput experimental techniques and computational methods have produced enormous protein interactions data [1], which have contributed to predict protein function [2,3] and detect protein complexes from protein–protein interaction (PPI) networks [4]. The Molecular Complex Detection (MCODE) algorithm [21] is one of the most classical seed expansion computational methods that can identify densely connected clusters in PPI networks. It first weights all nodes by their k-core neighborhood density as local network density, and expands from highest weighted node by adding nodes whose vertex weight percentage (VWP, weight percentage away from the weight of the seed vertex) is above a given threshold. The results show that SEGC outperforms other algorithms under full coverage in terms of both F-measure and accuracy with a real benchmark protein complex data set
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