Method for predicting protein compound on the basis of sample data

A protein complex and sample data technology, which is applied in the field of predicting protein complexes based on sample data, to achieve the effects of reducing noise, improving accuracy, and improving accuracy

Active Publication Date: 2018-10-19
CAPITAL NORMAL UNIVERSITY
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Problems solved by technology

[0003] However, accurate and efficient protein

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  • Method for predicting protein compound on the basis of sample data
  • Method for predicting protein compound on the basis of sample data
  • Method for predicting protein compound on the basis of sample data

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Embodiment 1

[0188] System Process Reference image 3 .

[0189] The protein interaction data is public data, and the PPI data set used by the inventor is the yeast protein interaction network, because the yeast data is relatively complete among all organisms. The DIP data set is used as the protein interaction data set, and GSE3431 is used as the gene expression data; the yeast genome database is used to calculate the GO semantic similarity; the CYC2008 database is used as the reference set.

[0190] S1: Construction of weighted network.

[0191] The PPI network is constructed based on the DIP data. In the DIP data set, there is a connection between the two proteins that interact. If there is no interaction between the two proteins, there is no connection between the two proteins. refer to Figure 6 , the nodes represent proteins, and the edges represent the interactions between proteins, Figure 6 (a) and (b) represent the unweighted PPI network and the weighted PPI network respectiv...

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Abstract

The invention relates to a method for predicting a protein compound on the basis of sample data. The method comprises the following steps that: (1) on the basis of the sample data, constructing a weighted PPI (Protein-Protein Interaction) network, and carrying out denoising processing on the weighted PPI networok, wherein the denoising processing is that the semantic similarity of gene ontology istaken as the weight of the PPI network for carrying out weighting; (2) on the basis of the weighted PPI network subjected to the denoising processing to construct a dynamic weighted PPI network; and(3) utilizing a hybrid clustering algorithm to predict the protein compound. By use of the method, through the introduction of a GO semantic similarity, the noise of PPI data can be effectively lowered, and in addition, the method has a biological meaning. Meanwhile, real protein activities are reflected, and the dynamic nature of the PPI network is embodied. In addition, the defect of overfittingor underfitting in a traditional clustering algorithm is eliminated, the accuracy of the clustering algorithm is improved, and therefore, the accuracy of a protein compound prediction result is effectively improved.

Description

technical field [0001] The present invention relates to the field of bioinformatics, in particular, the present invention relates to a method and system for predicting protein complexes based on sample data. Background technique [0002] A protein complex is a group of proteins that are bound together by interactions of multiple proteins at the same time or space. The number of protein complexes detected by experimental methods is small and the cost is high. How to use computational methods to mine effective protein complexes from a large amount of protein interaction data has become a new research direction. The prediction of protein complexes is of great significance for understanding cell life activities and protein functions. [0003] However, accurate and efficient protein complex prediction methods need further research. Contents of the invention [0004] This application is based on the inventor's discovery and recognition of the following facts and problems: [...

Claims

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Application Information

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IPC IPC(8): G06F19/24G06F19/18
CPCG16B20/00G16B40/00
Inventor 刘丽珍孙晓武宋巍
Owner CAPITAL NORMAL UNIVERSITY
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