Spectral clustering for detecting protein complexes in protein–protein interaction (PPI) networks

Qin, Guimin; Gao, Lin

doi:10.1016/j.mcm.2010.06.015

Cited by 37 publications

(20 citation statements)

References 11 publications

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“…Studies of protein complexes in a PPI network contribute greatly to the understanding of the biological mechanism. Spectral clustering has been widely applied to detect protein complexes from PPI networks [Christel and Kim 2005;Sen et al 2006;Kentaro et al 2010;Qin and Gao 2010].…”

Section: Protein Complexes Detection Resultsmentioning

confidence: 99%

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Sampling for Nyström Extension-Based Spectral Clustering

Zhang

Zong

You

et al. 2016

ACM Trans. Knowl. Discov. Data

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Sampling is the key aspect for Nyström extension based spectral clustering. Traditional sampling schemes select the set of landmark points on a whole and focus on how to lower the matrix approximation error. However, the matrix approximation error does not have direct impact on the clustering performance. In this article, we propose a sampling framework from an incremental perspective, i.e., the landmark points are selected one by one, and each next point to be sampled is determined by previously selected landmark points. Incremental sampling builds explicit relationships among landmark points; thus, they work together well and provide a theoretical guarantee on the clustering performance. We provide two novel analysis methods and propose two schemes for selecting-the-next-one of the framework. The first scheme is based on clusterability analysis, which provides a better guarantee on clustering performance than schemes based on matrix approximation error analysis. The second scheme is based on loss analysis, which provides maximized predictive ability of the landmark points on the (implicit) labels of the unsampled points. Experimental results on a wide range of benchmark datasets demonstrate the superiorities of our proposed incremental sampling schemes over existing sampling schemes.

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Section: Protein Complexes Detection Resultsmentioning

confidence: 99%

“…To evaluate the complexes mined by clustering, the resulting complexes are matched to known protein complexes from Gavin et al [2006] and Krogan et al [2006], 478 and 547 in number, respectively. We use the method in Qin and Gao [2010] to determine the number of clusters. The number of samples is set as 800.…”

Section: Protein Complexes Detection Resultsmentioning

confidence: 99%

Sampling for Nyström Extension-Based Spectral Clustering

Zhang

Zong

You

et al. 2016

ACM Trans. Knowl. Discov. Data

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“…Signal Transduction Model (STM) is the novel algorith m reflects the signal transduction that selects representative protein for every cluster and modify cluster iteratively based on signal transduction [9].TRIBE-M CL (Markov Clustering), CASCA DE, GFA are the other effective algorithm which are simulate a functional and biological flow of the protein. [10,11] This clustering approach converts the problem to quadratic optimi zation with some constraints through the matrix analysis methodology. These methods are generally useful for large and complex datasets.…”

Section: ) Density Based Clustering [1 6 and 15]mentioning

confidence: 99%

“…It has advantage of fast partitioning of the PPI network into appropriate biological cluster with approximate equal size. Qin et al [11] had developed a spectral clustering technique which is useful to perceive complexes of the proteins and functions. [12] Densely connected structure sometimes absent in the PPI network which becomes the problem for the functional module detection.…”

Section: ) Density Based Clustering [1 6 and 15]mentioning

confidence: 99%

A Review on Clustering Approaches for the Functional Module Detection From Ppi Network

2015

IJAERD

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Abstract-Proteins are the building bit of the living creatures and it assumes transcendent part to fulfill natural procedures of the life forms. Protein Protein Interaction (PPI) network is a sort of system that delineates the fundamental part in organic exercises. PPI is the corner stone of all the biological processes occurring in the organisms I.INTRODUCTIO N Bioinformatics is the interdisciplinary field which involves study and an alysis of biological data by using various fields such as mathematics, statistics, science, mining etc. In bioinformatics Protein -Protein Interaction (PPI) contributes a lot for analyzing the biological processes of the organisms. PPI helps a ton for examin ing the organic procedures of the life forms. Protein-Protein Interaction is a sort of system in wh ich proteins are communicating with each for the natural exercises which are done in the life forms. It is very critical to break down these proteins , collaborate with one another to complete metabolic exercises. For this reason utilitarian module recognition got to be fundamental.Proteins are the main and single constitute in the organisms that perform different functions. Proteins are involved in biological p rocesses and intermingle with each other. This interaction makes Protein-Protein Interaction network. PPI system can be demonstrated as an undirected graph G(V,E), where vertices are proteins and edges are protein communicat ions. PPI network are co mposed of various modules. Molecular biology is becoming a highly changing science in which identification of the functional module fro m the PPI network is crucial. Major Problem in PPI network is detection of functional modules fro m large network of PPI. It me ans mining or extracting some interesting cluster fro m large datasets. Identificat ion of the functional module fro m the existing network is the decisive problem to understand the function of the organisms [1]. In modern era, high-throughput experimental techniques have made notifiable advances in identifying function fro m the protein-protein interaction on the large scale. Co mputational techniques are utilized for catching useful modules for plotting the communicat ion of proteins to see how diverse proteins are schematized to perform various biological functions fro m higher level substructure of proteins. PPI network can be created fro m the analysis of the functional module detection. Thus it becomes omnipotent to identify functional module fro m the pair of proteins and its network.In the next section, the structure of a review paper is described. Section II contains Importance of functional module detection from PPI network. Section III contains major challenges for identifying modules. Sect ion IV contains associated research study in this area. Section V describes state-of-art methods. Section VI contains comparative study. Section VII contains conclusion of the paper. II. IMPO RTANCE O F FUNCTIO NAL MO DULE DETECTIO NThe twenty first century is the era of natural life science. With the ultimate of sequenc...

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“…Spectral clustering-based approaches solve the problem by converting it to a quadratic optimization with constraints by utilizing the methodology of matrix analysis. For instance, Qin et al presented a spectral clustering method in [7], and a diffusion model-based spectral clustering was proposed in [8]. However, using the algorithms mentioned above, protein modules with densities less than certain thresholds may be absent from some potential protein modules in PPI networks.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks

Liu

Yang

et al. 2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Swarm intelligence algorithms have been successfully applied to the detection of functional modules in PPI networks. As the increasing of the PPI network size, those algorithms will cost more time in functional module detection. In this paper, we present a novel algorithm, ACC-MLF, which combines ant colony clustering with multilevel framework to reduce the runtime in the large-scale PPI networks. First, use a new matching strategy to coarsen the original large-scale PPI network, and get a smaller PPI network. Then, use the ant colony clustering algorithm to cluster the obtained network. Finally, get the clustering result of original network through de-coarsening and use the refinement to avoid the result from falling into the local optimal. Experiments in some large-scale networks show that the detecting speed of ACC-MLF has significantly improved in contrast to ACC-FMD, and ACC-MLF can get better clustering results in some evaluation metrics while compared with ACC-FMD, MCODE, MINE and Core algorithms.

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Spectral clustering for detecting protein complexes in protein–protein interaction (PPI) networks

Cited by 37 publications

References 11 publications

Sampling for Nyström Extension-Based Spectral Clustering

Sampling for Nyström Extension-Based Spectral Clustering

A Review on Clustering Approaches for the Functional Module Detection From Ppi Network

Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks

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