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A Key Protein Identification Method Based on Tensor Random Walk

A technology of random walk and identification method, applied in the field of systems biology, can solve the problem of poor prediction performance of key proteins, and achieve the effect of good prediction performance

Active Publication Date: 2022-04-15
CHANGSHA UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to provide a key protein identification method based on tensor random walk, to solve the technical defects of poor key protein prediction performance in the prior art

Method used

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  • A Key Protein Identification Method Based on Tensor Random Walk
  • A Key Protein Identification Method Based on Tensor Random Walk
  • A Key Protein Identification Method Based on Tensor Random Walk

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Embodiment 1

[0047] see figure 1 , the present invention firstly provides a key protein identification method based on tensor random walk, comprising the following steps:

[0048] S1: Obtain protein interaction network topology, protein domain information, time-series-based gene expression information, and protein homology information.

[0049] The above three kinds of data all come from public databases on the Internet. Protein interaction networks derived from Saccharomyces cerevisiae (baker's yeast) have been well characterized by gene knockout experiments and have been widely used for the assessment of key proteins. The protein domain data were downloaded from the Pfam database, containing 1107 distinct domains involving 3,056 proteins in the PPI network. The gene expression data contains a total of 6,776 gene products (proteins) sampling data at 36 different times.

[0050] S2: According to the protein interaction network topology, protein domain information and gene expression inf...

Embodiment 2

[0088] In order to verify the effectiveness of the key protein identification method proposed in the present invention, we run this method and other ten current key protein identification methods on the yeast protein interaction network. The protein interaction network used for the experiments is derived from the DIP database, which consists of 5,023 proteins and 22,570 edges. Self-interactions and repeated interactions have been removed from the network. The gene expression data of yeast contains the sampling data of 6,776 gene products (proteins) at 36 different times. Of the 6,776 proteins, 4,902 proteins were included in the DIP dataset. image 3It is the method proposed by the present invention (TPR) and other ten key protein prediction methods DC, IC, BC, CC, SC, NC, CoEWC, Pec, POEM, ION respectively predict the top 100, 200, 300, 400, 500, 600 Accuracy comparison graphs of key proteins (ie n=100, 200, 300, 400, 500, 600).

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Abstract

The invention discloses a method for identifying key proteins based on tensor random walk, which includes the following steps: obtaining protein interaction network topology, protein structural domain information, gene expression information based on time series, and protein homology information; according to the above Information, establish the association relationship between different protein nodes in the protein node interaction; initialize the hub score of the protein node according to the protein homology information; establish a tensor model based on the association relationship between different protein nodes in the protein interaction; The tensor model is iteratively calculated to obtain the hub score of each protein node and sorted, and the top n protein nodes in the sequence are regarded as key proteins. The invention is simple and effective. Compared with other methods and tested on multiple data sets, it shows that the invention has better predictive performance in identifying key proteins.

Description

technical field [0001] The invention relates to the field of systems biology, in particular to a key protein identification method based on tensor random walk. Background technique [0002] Protein is an essential component of all cell and tissue structures, and the most important material basis for life activities. However, the importance of different proteins to life activities is not the same. Usually those proteins that cause the loss of function of the protein complex after being deleted and cause the organism to fail to survive or develop are called key proteins. Key proteins are not only necessary for the survival and reproduction of organisms, but also play an important role in life activities. Therefore, the identification of key proteins helps to understand the internal organization and process of life activities at the system level. At the same time, a large number of studies have shown that key proteins (genes) are often disease-causing genes. It can be seen ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B5/00G16B50/20G16B25/10
Inventor 赵碧海胡赛王雷李学勇张帆田清龙
Owner CHANGSHA UNIVERSITY
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