Recent advances in clustering methods for protein interaction networks

Wang, Jianxin; Li, Min; Deng, Youping; Pan, Yi

doi:10.1186/1471-2164-11-s3-s10

Cited by 122 publications

(82 citation statements)

References 118 publications

(218 reference statements)

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“…The choice of the disable properties (measure) for the clusters to be identified depends on the available data and on the corresponding network organization (model). Accordingly, there is a great variety of methods proposed for finding clusters with a selected measure (see reviews by Schaeffer, 2007 andWang et al, 2010). Below we describe several examples to be further compared with our method.…”

Section: Introductionmentioning

confidence: 99%

Detecting Non-Uniform Clusters in Large-Scale Interaction Graphs

Lev-Tov

Amberkar²,

Frenkel

et al. 2014

Journal of Computational Biology

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Graph clustering becomes difficult as the graph size and complexity increase. In particular, in interaction graphs, the clusters are small and the data on the underlying interaction are not only complex, but also noisy due to the lack of information and experimental errors. The graphs representing such data consist of (possibly overlapping) clusters of non-uniform size with some false positive and false negative links. In this article, we propose a new approach, assuming that clusters in the graphs of protein-protein interaction (PPI) networks resemble corrupted cliques. Therefore, the problem can be reduced to looking for clusters only among nodes of approximately similar degrees. This idea was implemented using a soft version of the Farthest-Point-First (FPF) clustering algorithm with the Jaccard distance function modified to perform on slightly overlapping clusters. The StripClust program developed by us was tested on a synthetic network and on the yeast PPI network.

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Section: Introductionmentioning

confidence: 99%

Detecting Non-Uniform Clusters in Large-Scale Interaction Graphs

Lev-Tov

Amberkar²,

Frenkel

et al. 2014

Journal of Computational Biology

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“…We presented an algorithm named HC-PIN [15] to generate protein complexes by using edges clustering coefficient from both weighted and unweighted graphs. More protein complex discovery algorithms can be referred to in [16,17]. Although different types of clustering algorithms have their own advantages, these algorithms based on dense subgraphs have much better performance than those based on other topological structure.…”

Section: Introductionmentioning

confidence: 99%

Identifying dynamic protein complexes based on gene expression profiles and PPI networks

Chen

Wang

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of "closeness" and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.

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“…Clustering of PPINs is aimed at identifying two types of cellular modules: protein complexes and functional modules [1]. Protein complexes are groups of proteins that interact with each other at the same time and place, forming a unique multi-molecular machine.…”

Section: Introductionmentioning

confidence: 99%

“…Another requirement from the algorithm is that it should be fast enough and scalable, to make possible its application during the EN creation, where the calculation speed is a crucial parameter. There is a various graph clustering methods which are applied for the analysis of biological, in particular PPI networks (see [1], [9], [10]). However, there is no universal approach which can be satisfactory for all cases.…”

Section: Introductionmentioning

confidence: 99%

Application of a K-Ladder Connectivity Algorithm for Clustering of Protein Evolutionary Network

Nibhani¹,

Soffer²,

Mu’alem³

et al. 2014

IJMO

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Abstract-An evolutionary network (EN) in formatted protein sequence space is a very large graph representing information about sequence similarity of relatively short protein fragments. This graph can be used for detecting hidden relatedness between proteins, which is highly significant in protein annotation. Effective EN analysis requires an appropriate graph clustering approach. Based on the fact that biological relatedness is strongly dependent on the number of independent graph nodes connections, we develop a network clustering method that is capable to produce quality clusters the members of which have a satisfactory level of relatedness.In this article we describe a new network partitioning method which is based on the k-cycles graph connectivity approach. After formally defining a unique structure, named k-ladder connectivity, we demonstrate that the k-ladder-based algorithm is able to successfully detect the groups of functionally related proteins.To exhibit the quality of the method, we have conducted a set of experiments in which it has been very effective in clustering of EN, as well as the significantly denser protein-protein interaction networks (PPINs). Furthermore, it can be simply adapted for more complicated structures than cycles, as well as applied to other large networks of different types.Index Terms-K-ladder, connectivity algorithm, network clustering, protein evolutionary network, formatted protein sequence space, protein-protein interaction networks. I. INTRODUCTIONProteins are the main components in all living organisms. Significant progress in molecular genetic technology during the last decade provided us with a vast amount of protein sequences that exist in nature. For example, the recent release of the UniProt database (http://www.uniprot.org/) contains more than 40,000,000 protein sequences. However, many of these protein sequences have no proper annotation -meaning that the structure and biological function of the corresponding proteins are unknown. Such characterization of these proteins on the basis of their known sequences and often according to some other high-throughput information is one of the main challenges in computational biology.Among a multitude of bioinformatics methods and algorithms dedicated to reveal the sought-after protein organization and biological functionality, there is a group of approaches that use graph analysis techniques applied to various kinds of protein associated networks. A common Manuscript received May 14, 2014; revised July 19, 2014. This work was supported by the European Union seventh framework program via the PathoSys Project (grant number 260429).The authors are with the ORT Braude College of Engineering, Karmiel, Israel and Research Fellow at Institute of Evolution, University of Haifa, Israel (e-mail: reshma.iidsalld2007@gmail.com, asoffer@braude.ac.il and ahumu@yahoo.com, vlvolkov@braude.ac.il, zakharf@research.haifa.ac.il).example of such a network is the Protein-Protein Interaction network (PPI or PPIN), a dedicated graph used for integ...

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Recent advances in clustering methods for protein interaction networks

Cited by 122 publications

References 118 publications

Detecting Non-Uniform Clusters in Large-Scale Interaction Graphs

Detecting Non-Uniform Clusters in Large-Scale Interaction Graphs

Identifying dynamic protein complexes based on gene expression profiles and PPI networks

Application of a K-Ladder Connectivity Algorithm for Clustering of Protein Evolutionary Network

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