Method for identifying key proteins with AFSO (artificial fish school optimization) algorithm

An optimization algorithm and artificial fish swarm technology, applied in the field of biological information, can solve the problems of lack of global and overall grasp, failure to consider the reliability of protein interaction network, low identification accuracy of key proteins, etc., to achieve accurate identification.

Active Publication Date: 2018-03-09
SHAANXI NORMAL UNIV
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Problems solved by technology

[0008] Based on the defects of the above key protein identification methods, the main reason is whether the reliability of the protein interaction network is considered, only some features are considered, and the overall and overall grasp is lacking, and the accuracy of key protein identification is low.

Method used

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  • Method for identifying key proteins with AFSO (artificial fish school optimization) algorithm
  • Method for identifying key proteins with AFSO (artificial fish school optimization) algorithm
  • Method for identifying key proteins with AFSO (artificial fish school optimization) algorithm

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Embodiment 1

[0110] Taking protein network as an example, the steps of a method for identifying key proteins based on artificial fish swarm optimization algorithm are as follows:

[0111] In this example, the yeast data set (DIP 20160114 version) collected from the DIP database was used as the simulation data set, and the DIP data contained 5028 proteins and 22303 interaction relationships. The gene expression dataset is collected from the yeast metabolic expression dataset GSE3431 in the GEO database, which includes 9336 genes, gene values ​​at 36 time points in 3 cycles, covering 95% of the proteins in DIP. GO data includes annotation spectrum and SGD. Known protein complex information is from CYC2008, including 408 protein complexes, covering 1492 proteins. Key protein data are obtained by integrating data from four databases: MIPS, SGD, DEG and SGDP , contains a total of 1285 key proteins, corresponding to 1152 of the 5028 proteins are key proteins, and the rest are regarded as non-key...

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Abstract

The invention discloses a method for identifying key proteins with an AFSO (artificial fish school optimization) algorithm. The method comprises steps as follows: a protein-protein interaction networkis converted into an undirected graph, a purified protein-protein interaction network is constructed, RNA gene expression values corresponding to proteins, GO comment information and degrees of proteins in known compounds are obtained, edges and nodes of the purified protein-protein interaction network are treated, known key proteins are selected as initial artificial fishes, the artificial fishes execute foraging behavior, random behavior, following behavior and swarm behavior, and the key proteins are produced. According to the method, the key proteins can be identified accurately; a simulation experiment result indicates that performance of indexes such as sensitiveness, specificity, a positive predictive value, a negative predictive value and the like is better; compared with other methods for identifying the key proteins, the method has the advantages that optimizing characteristics of artificial fish schools are combined with topological characteristics of the protein-protein interaction network to realize the key protein identification process, and the accuracy rate of the key protein identification is increased.

Description

technical field [0001] The invention belongs to the field of biological information, and in particular relates to a method for identifying key proteins based on an artificial fish swarm optimization algorithm. Background technique [0002] Key proteins are the products of key genes and are an essential part of organisms to maintain life activities. The absence of key proteins will lead to the failure of life activities and even the death of organisms. The prediction and identification of key proteins is a research work of great significance. On the one hand, it helps to study the growth regulation process related to cells; on the other hand, it also has far-reaching significance for disease diagnosis and drug design. Initially, the identification of key proteins was mainly through biological experimental methods, such as single gene knockout and RNA interference. Although the identification of key proteins through these experimental techniques is accurate and effective, it ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/12G06F19/18G06N3/00
CPCG06N3/006G16B5/00G16B20/00
Inventor 雷秀娟杨晓琴代才程适
Owner SHAANXI NORMAL UNIV
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