Identification of functional modules in a PPI network by clique percolation clustering

Zhang, Shihua; Ning, Xue-Mei; Xia, Yu

doi:10.1016/j.compbiolchem.2006.10.001

Cited by 64 publications

(41 citation statements)

References 24 publications

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“…A unique feature of the clique percolation clustering method (CPM) is that it can uncover the overlapping community structure of complex networks, i.e., one node can belong to several communities[37]. In order to detect the densely connected regions in network with possible overlap and their functions in the network, the PPI information data were imported into CFinder-2.0.6 (an open source software platform in CPM method), and the clustering analysis was easily performed.…”

Section: Methodsmentioning

confidence: 99%

The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis

et al. 2017

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Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.

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Section: Methodsmentioning

confidence: 99%

The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis

et al. 2017

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“…P overlap ðX ¼ kÞ represents the probability of a random overlap between the predicted modules and experimentally determined modules (i.e. either protein complexes or functional modules; Spirin and Mirny 2003;Zhang et al 2006). The random variable X in P overlap ðX ¼ kÞ follows the hypergeometric distribution with parameters N; n1 and n2.…”

Section: Modules Found In the Yeast Protein Interaction Datamentioning

confidence: 99%

ModuleSearch: finding functional modules in a protein–protein interaction network

Cui

Shrestha

Han

2012

Computer Methods in Biomechanics and Biomedical Engineering

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Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein-protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein-protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.

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“…2) Choice Seeds From Weighted PIN: Various approaches have been developed to identify the seeds of communities [14]- [17], Baumes et al. [20], [21]'s Iterative Scan algorithm selects edges as seeds randomly and expands seeds until the new seeds produce communities that are duplicates of previously-found communities.…”

Section: ) Utilize Available Data For Building Weighted Pinmentioning

confidence: 99%

A Novel Protein Complex Identification Algorithm Based on Connected Affinity Clique Extension (CACE)

Zhao

et al. 2014

IEEE Trans.on Nanobioscience

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A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity which is inferred from protein complexes is interpreted as the reliability and possibility of interaction. The protein interaction network is constructed as a weighted graph, and the weight is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effectively and accurately when compared with other state-of-art algorithms CPM and IPC-MCE.

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Identification of functional modules in a PPI network by clique percolation clustering

Cited by 64 publications

References 24 publications

The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis

The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis

ModuleSearch: finding functional modules in a protein–protein interaction network

A Novel Protein Complex Identification Algorithm Based on Connected Affinity Clique Extension (CACE)

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