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Gene splicing site identification model constructing method based on particle swarm optimization twin support vector machine

A technology of support vector machine and particle swarm optimization, which is applied in the field of gene cutting and machine learning, can solve the problems of blindness in parameter selection and difficulty in parameter setting of Gemini support vector machine, etc.

Pending Publication Date: 2020-07-07
MINJIANG UNIV
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Problems solved by technology

Secondly, in view of the difficulty in setting the parameters of the Gemini SVM in the process of classification and recognition, the particle swarm optimization algorithm is used to optimize the parameters of the Gemini SVM, so as to further improve the recognition performance of the splice site and avoid the blindness of its parameter selection sex

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  • Gene splicing site identification model constructing method based on particle swarm optimization twin support vector machine
  • Gene splicing site identification model constructing method based on particle swarm optimization twin support vector machine
  • Gene splicing site identification model constructing method based on particle swarm optimization twin support vector machine

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[0033] In order to make the features and advantages of this patent more obvious and easy to understand, the following special examples are described in detail as follows:

[0034] Such as image 3 As shown, this embodiment constructs a gene splicing site identification model based on particle swarm optimization twin support vector machine, which specifically includes the following steps:

[0035] Step S1: Select the DNA fragments that conform to the GT-AG rule, and divide the DNA sequences of the real and false splice acceptor sites into the acceptor site training set and the acceptor site test set; The sequences are divided into a donor site training set and a donor site test set;

[0036] Step S2: Preprocessing the sequence data of each training set and test set;

[0037] Step S3: performing feature extraction on the preprocessed sequence data;

[0038] Step S4; according to the characteristics of the sequence data extracted in step S3 in the training set, use TWSVM based...

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Abstract

The invention provides a gene splicing site identification model constructing method based on a particle swarm optimization twin support vector machine. Firstly splicing site identification is used asa two-class machine learning identification problem, and furthermore identification is finished through splicing sequence characteristic next to a site. Secondly, for aiming at a problem of relatively difficult twin support vector machine parameter setting in a class identification process, a particle swarm algorithm is used for parameter optimization of the twin support vector machine, thereby further improving the identification performance of the splicing site and preventing parameter selecting blindness. An experiment result verifies feasibility of the method and can effectively improve splicing site identification rate and accuracy.

Description

technical field [0001] The invention belongs to the fields of gene splicing and machine learning, and in particular relates to a method for constructing a gene splicing site recognition model based on particle swarm optimization twin support vector machines. Background technique [0002] Since the beginning of the 21st century, modern molecular biology based on the DNA double helix structure has made great progress and development. The determination of the entire human gene sequence has become a milestone global joint research achievement. Driven by the Human Genome Project, a project of the century, data related to molecular biology has experienced explosive growth. As an interdisciplinary subject, bioinformatics using computer as a tool has become a hot research direction. With the generation of more and more whole genome sequence data, the difficulty of gene prediction based on genome sequence is also increasing. [0003] RNA splicing is a very important biological pro...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/30G16B40/20G06K9/62G06N3/00
CPCG16B20/30G16B40/20G06N3/006G06F18/2411G06F18/2451
Inventor 张福泉
Owner MINJIANG UNIV
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