Plant salt-alkaline resistance gene identification method and system

By constructing a machine learning method based on the C4.5 algorithm model, and using feature vectors to identify plant salt-alkali resistant genes, the problem of low identification accuracy in traditional methods is solved, achieving efficient and low-cost gene identification and promoting crop resistance breeding.

CN115295081BActive Publication Date: 2026-06-09NORTHEAST FORESTRY UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHEAST FORESTRY UNIV
Filing Date
2022-09-06
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing methods for identifying plant salt-alkali resistance genes have high false positive and false negative rates, resulting in low identification accuracy and failing to meet the need for efficient mining of superior gene resources.

Method used

A C4.5 algorithm model was constructed using machine learning algorithms. The plant protein sequences were trained using feature vectors DR106, DPC229, EIIP1, ZS1636, and ZS8923 to identify salt-tolerant genes. The process included obtaining feature vectors, building the model, and identification.

Benefits of technology

It improves the accuracy of salt-alkali resistance gene identification, reduces costs and time, and can better process genomic data under high-throughput sequencing technology, thus promoting the genetic improvement of crop resistance.

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Abstract

The application discloses a plant salt-alkali-resistant gene recognition method and system, in particular to a plant salt-alkali-resistant gene recognition method and system based on machine learning, which aims at solving the problem of low accuracy of plant salt-alkali-resistant functional gene recognition caused by high false positive rate and false negative rate of the recognition result of the salt-alkali-resistant gene recognition method which depends on the recognition of plant homologous genes, and comprises the following steps: obtaining a plurality of plant protein sequences known to be salt-alkali-resistant genes or not; obtaining a feature vector of the plant protein sequence; constructing a C4.5 algorithm model, training the C4.5 algorithm model with the feature vector, outputting whether the gene is a salt-alkali-resistant gene, and obtaining the trained C4.5 algorithm model; executing S2 on the plant protein sequence to be recognized to obtain a feature vector, inputting the feature vector into the trained C4.5 algorithm model, and obtaining whether the plant protein sequence to be recognized contains a salt-alkali-resistant gene. The system executes any step of the method. The application belongs to the field of gene recognition.
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