A computational approach to predict key proteins by fusing multiple data features

A protein and multi-data technology, applied in the cross-field of mathematics and biology, can solve the problems of predicting key protein accuracy and efficiency to be improved, and achieve the effect of solving expensive costs and long time periods

Active Publication Date: 2021-07-02
EAST CHINA JIAOTONG UNIVERSITY
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  • Abstract
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Problems solved by technology

[0006] Although the above-mentioned comprehensive multi-data source method has improved the accuracy of predicting key proteins, the accuracy and efficiency of predicting key proteins still need to be improved

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  • A computational approach to predict key proteins by fusing multiple data features
  • A computational approach to predict key proteins by fusing multiple data features
  • A computational approach to predict key proteins by fusing multiple data features

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Embodiment Construction

[0020] The beneficial effects of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, aiming at helping readers better understand the essence of the present invention, but not limiting the implementation and protection scope of the present invention.

[0021] In view of the fact that the protein interaction relationship data and key protein data of yeast are the most complete among all species at present, in order to test the effectiveness of the present invention, the yeast data is used for the next verification analysis. The yeast protein interaction relationship data used for testing in the present invention comes from the data of the DIP database in October 2010. After eliminating the repeated and self-interaction data, a protein interaction relationship containing 5093 proteins and 24743 links is finally obtained. Act on network data;

[0022] Download yeast gene expression data (GSE3431) from the ...

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Abstract

The invention discloses a calculation method for predicting key proteins by fusing multiple data sources. By analyzing the characteristics of aggregation, co-expression, functional similarity, and position consistency of key proteins, the method effectively integrates the edge clustering coefficient of protein interaction network, Pearson correlation coefficient of gene expression value, and gene ontology terminology. Semantic similarity index and statistical features of protein subcellular localization. The method of the present invention is simple and easy to use, input the four kinds of data of protein interaction relationship data, gene expression spectrum data, gene ontology term information data and protein subcellular localization data information, after testing and verification, compared with the existing method, the proposed method of the present invention The method can significantly improve the prediction accuracy and efficiency of key proteins in protein interaction networks.

Description

technical field [0001] The invention relates to the cross field of mathematics and biology, in particular to a calculation method for identifying key proteins in a protein interaction network based on various biological data sources. Background technique [0002] Proteins are the genetic organic substances that make up cells, are the main participants in life activities, and play a very important role in maintaining normal physiological activities. The key protein is the protein that is necessary to maintain the normal life activities of the organism. Once this kind of protein is abnormal, it will lead to the disorder of the normal physiological activities of the organism and even cause diseases. Studies have shown that key proteins are inextricably linked to disease-causing genes, drug target design, and personalized medical treatment. Effective identification of key proteins is helpful for the study of disease pathogenesis and drug molecular targets. [0003] Traditional ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B20/30
Inventor 张伟徐佳
Owner EAST CHINA JIAOTONG UNIVERSITY
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