Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks. In this paper, we propose a Multi-source Learning based Protein Community Detection (MLPCD) algorithm by integrating Gene Expression Data (GED) and a parallel solution of MLPCD using cloud computing technology. To effectively discover the biological functions of proteins that participating in different cellular processes, GED under different conditions is integrated with the original PPI network to reconstruct a Weighted-PPI (WPPI) network. To flexibly identify protein communities of different scales, we define community modularity and functional cohesion measurements and detect protein communities from WPPI using an agglomerative method. In addition, we respectively compare the detected communities with known protein complexes and evaluate the functional enrichment of protein function modules using Gene Ontology annotations. Moreover, we implement a parallel version of the MLPCD algorithm on the Apache Spark platform to enhance the performance of the algorithm for large-scale realistic PPI networks. Extensive experimental results indicate the superiority and notable advantages of the MLPCD algorithm over the relevant algorithms in terms of accuracy and performance.
@cite_6: Motivation: Clustering algorithms play an important role in the analysis of biological networks, and can be used to uncover functional modules and obtain hints about cellular organization. While most available clustering algorithms work well on biological networks of moderate size, such as the yeast protein physical interaction network, they either fail or are too slow in practice for larger networks, such as functional networks for higher eukaryotes. Since an increasing number of larger biological networks are being determined, the limitations of current clustering approaches curtail the types of biological network analyses that can be performed. Results: We present a fast local network clustering algorithm SPICi. SPICi runs in time O(V log V+E) and space O(E), where V and E are the number of vertices and edges in the network, respectively. We evaluate SPICi's performance on several existing protein interaction networks of varying size, and compare SPICi to nine previous approaches for clustering biological networks. We show that SPICi is typically several orders of magnitude faster than previous approaches and is the only one that can successfully cluster all test networks within very short time. We demonstrate that SPICi has state-of-the-art performance with respect to the quality of the clusters it uncovers, as judged by its ability to recapitulate protein complexes and functional modules. Finally, we demonstrate the power of our fast network clustering algorithm by applying SPICi across hundreds of large context-specific human networks, and identifying modules specific for single conditions. Availability: Source code is available under the GNU Public License at http: compbio.cs.princeton.edu spici Contact: ude.notecnirp.sc@anom Supplementary information: Supplementary data are available at Bioinformatics online.
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@cite_16: We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.
@cite_13: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
@cite_17: Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803–1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651–654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910–913], and hence focused on single two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein–protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47–C52], suggesting that found modules constitute the “building blocks” of molecular networks.
Existing studies demonstrate that the genes or gene products with similar expression patterns tend to have the similar biological function in a period of life, and also more likely to contact each other to form a dense functional module in PPI networks @cite_2 . Therefore, proteins' GED data are used in this work to evaluate the similarity of proteins in a PPI network @cite_15 @cite_17 . Ji . @cite_15 introduced a multiple-grain model to detect functional modules from large-scale PPI networks. Spirin . @cite_17 presented an enumeration method to find completely connected subgraphs, and then to search for protein functional module. GN algorithm @cite_13 is a classical community discovery algorithm. Depending on high cohesion for internal communities and low cohesion among communities, structures of cohesive communities are relatively detected by gradually removing the edges among communities. Louvain @cite_16 is a fast aggregation algorithm that is used to extract the community structure in large networks, using a heuristic method based on modularity optimization. SPICi @cite_6 is a clustering algorithm for discovering protein complexes and functional modules from PPI networks.