Weight assembly clustering method for excavating protein complex

A clustering method and protein technology, applied in the field of systems biology, to reduce sensitivity and dependence and enhance interpretability

Inactive Publication Date: 2013-08-07
SUN YAT SEN UNIV
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Since different clustering results were not screened and only one clustering meth

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  • Weight assembly clustering method for excavating protein complex
  • Weight assembly clustering method for excavating protein complex
  • Weight assembly clustering method for excavating protein complex

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Embodiment 1

[0136] Nine classic protein complex mining algorithms (ClusterONE, CMC, COPRA, DPClus, MCL, MCODE, MINE, RNSC, SPICi) were selected to work on three yeast protein interaction network databases (Collins, Gavin and BioGRID). Two reference protein complex databases (MIPS and SGD) and three evaluation criteria (f-measure, Jaccard and PR) were used to verify the accuracy of the results of different algorithms. The statistical characteristics of the three protein interaction network databases and two reference databases corresponding to these three networks are shown in Table 2 and Table 3. Among the three evaluation criteria, f-measure measures the similarity between the predicted complex and the complex in the reference library from the protein complex level. Jaccard and PR measured the match between the predicted complex and the complex in the reference library at the complex-protein level.

[0137] Before describing these evaluation criteria, we give some notation explanations....

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Abstract

The invention discloses a weight assembly clustering method for excavating a protein complex. The method comprises the following steps: inputting a protein interaction network to produce an undirected graph, selecting m clustering methods to be applied to the network to obtain m clustering results; rebuilding characteristic networks for the base clustering results to obtain m characteristic networks, wherein the m characteristic networks correspond to m characteristic matrixes; combing the m characteristic matrixes to obtain a combined matrix W, wherein uq refers to weights of the qth characteristic network, and uq is larger than or equal to 0, the combined matrix W corresponds to a new network, and elements Wi and j are used for measuring the similar degree of the protein i and the protein j in the new network; excavating the clusters in the new network through adopting a Bayes non-negative matrix factorization algorithm; integrating the weight studying and the complex discovering into an optimal object, so that the weight is optimized through the clustering result, otherwise, the clusters are guided by the weight results; and obtaining the final protein complex excavating result after the optimizing is finished.

Description

technical field [0001] The invention belongs to the field of systems biology, and relates to a method for mining protein complexes, more specifically, to a weighted assembly clustering method for mining protein complexes. Background technique [0002] Protein is the executor and regulator of molecular functions, and also the main carrier of life activities. Proteins seldom function as monomers, but interact with other proteins to form complexes or functional modules to perform biological functions cooperatively. The mining of protein complexes not only helps to understand the functional organization mechanism of cells, but also helps to reveal the pathogenesis of complex diseases. Although researchers can more accurately determine relatively stable protein complexes in a certain environment through chemical experiments, the interactions between proteins in some complexes are dynamically changing, that is, there are unstable protein complexes. Experiment-based research meth...

Claims

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Application Information

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IPC IPC(8): G06F19/12
Inventor 欧阳乐戴道清张晓飞
Owner SUN YAT SEN UNIV
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