Machine Learning-Based identification of resistance genes associated with sunflower broomrape

文献类型： 外文期刊

第一作者： Che, Yingxue

作者： Che, Yingxue;Zhang, Congzi;Xing, Jixiang;Xi, Qilemuge;Zuo, Yongchun;Shao, Ying;Zhao, Lingmin;Guo, Shuchun

作者机构：

关键词： Machine learning; Feature selection; Resistance genes; Sunflower broomrape

期刊名称：PLANT METHODS （ 影响因子：4.4； 五年影响因子：5.7 ）

ISSN：

年卷期： 2025 年 21 卷 1 期

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收录情况： SCI

摘要： BackgroundSunflowers (Helianthus annuus L.), a vital oil crop, are facing a severe challenge from broomrape (Orobanche cumana), a parasitic plant that seriously jeopardizes the growth and development of sunflowers, limits global production and leads to substantial economic losses, which urges the development of resistant sunflower varieties.ResultsThis study aims to identify resistance genes from a comprehensive transcriptomic profile of 103 sunflower varieties based on gene expression data and then constructs predictive models with the key resistant genes. The least absolute shrinkage and selection operator (LASSO) regression and random forest feature importance ranking method were used to identify resistance genes. These genes were considered as biomarkers in constructing machine learning models with Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Gaussian Naive Bayes (GaussianNB). The SVM model constructed with the 24 key genes selected by the LASSO method demonstrated high classification accuracy (0.9514) and a robust AUC value (0.9865), effectively distinguishing between resistant and susceptible varieties based on gene expression data. Furthermore, we discovered a correlation between key genes and differential metabolites, particularly jasmonic acid (JA).ConclusionOur study highlights a novel perspective on screening sunflower varieties for broomrape resistance, which is anticipated to guide future biological research and breeding strategies.

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