Method for identifying biological essential protein

A protein and biological technology, applied in the field of life sciences, can solve problems such as troublesome and complex verification of model correctness, and achieve the effect of improving accuracy, reducing training time and

Pending Publication Date: 2022-03-25
GUIZHOU UNIV +1
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Problems solved by technology

[0006] The purpose of the present invention is to provide a method for identifying biologically necessary proteins to overcome the time-consuming training of the identification process of key proteins found and described in the current prior art, and the complex and cumbersome defects of model correctness verification

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  • Method for identifying biological essential protein
  • Method for identifying biological essential protein
  • Method for identifying biological essential protein

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Embodiment Construction

[0071] The object of the present invention is to provide a method for identifying biologically essential proteins, comprising the following steps,

[0072] S1 data download:

[0073] The present invention obtains yeast protein information from DIP and GAVIN data sets respectively. After removing self-interactions and repeated interactions, the DIP dataset resulted in 5093 proteins, 24743 pairs of interactions and 1167 essential proteins. The GAVIN dataset provides 1855 proteins, 7669 pairs of interactions, and 714 basic proteins.

[0074] In addition, information on homologous proteins was downloaded from the InParanoid database (Version 7), which contains 100 genome-wide pairwise comparisons. Additionally, gene expression data for yeast were downloaded from the dataset provided by Tu BP.

[0075] Finally, the present invention will further download a data set containing 1285 Saccharomyces cerevisiae essential genes from four databases of MIPS, SGDP, DEG and SGD as a benchm...

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Abstract

The invention provides a method for constructing necessary protein recognition between multi-dimensional biological attribute information and PPI topological characteristics by using a deep neural network, and in order to solve the problem that gene expression data is not comprehensive, absent data is supplemented so as to improve robustness. And the convergence speed of the deep neural network is reduced by respectively constructing a PPI network topology structure, a Pearson's correlation coefficient and a homologous correlation coefficient. And finally, searching an optimal incidence relation of three features of the degree of the node, the Pearson's correlation coefficient and the homologous correlation coefficient through the deep neural network, thereby improving the recognition precision of the essential protein.

Description

technical field [0001] The invention belongs to the field of life sciences, and in particular relates to a method for identifying biologically necessary proteins. Background technique [0002] Protein is an essential element for body activity. In the process of life, proteins are closely connected to each other to complete a series of physiological activities, and finally form a protein network (protein-protein interaction, PPI). There are some essential proteins in the protein network. After the mutation is removed, the associated functions of the body will be lost, resulting in the body not functioning normally. Therefore, the prediction of key proteins based on the PPI network has a theoretical basis for the exploration of disease-causing genes and the development of drug targets. [0003] In the early days, identifying important proteins occurred mainly in biological experiments. Although bioassay techniques are highly accurate, such experiments are time-consuming and...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/30G06N3/08G06N3/04
CPCG16B20/30G06N3/08G06N3/045
Inventor 邹赛肖蕾贾伟谢明山王雷
Owner GUIZHOU UNIV
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