您好，欢迎访问广西农业科学院 机构知识库！

DualF-PBR: Dual-Extracting Protein Sequence Features for Predicting Plant Resistance Proteins

文献类型： 外文期刊

作者： Fang, Hui 1 ; Chen, Danyang 3 ; Tang, Chunyan 3 ; Li, Min 5 ; Zhong, Cheng 3 ;

作者机构： 1.Guangxi Univ, Med Coll, Nanning 530004, Peoples R China

2.Guangxi Univ, State Key Lab Conservat & Utilizat Subtrop Agrobio, Nanning 530004, Peoples R China

3.Guangxi Univ, Sch Comp Elect & Informat, Nanning 530004, Peoples R China

4.Guangxi Univ, Key Lab Parallel Distributed & Intelligent Comp Gu, Nanning 530004, Peoples R China

5.Guangxi Acad Agr Sci, Guangxi Crop Genet Improvement & Biotechnol Lab, Nanning 530007, Peoples R China

关键词： Feature extraction; Immune system; Protein sequence; Encoding; Neural networks; Amino acids; Data mining; Vectors; Pathogens; Bioinformatics; Plant resistance protein; Prediction; extracting features; disease-resistant breeding

期刊名称：IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

ISSN：

年卷期： 2025 年 22 卷 4 期

页码：

收录情况： SCI

摘要： Plant resistance proteins are evolved during growth and development to cope with complex environmental changes and infection of pathogens. Predicting plant resistance proteins is of great significance for further exploring plant disease resistance mechanism against viruses. In this paper, we propose a method for predicting plant resistance protein by dual-extracting features. The dual-extracted features are composed of the features extracted by modeling self-attention neural network and detecting sequence structure information respectively to obtain 2381-dimensional protein sequence features. We utilize the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to eliminate redundant features from the extracted 2381-dimensional features to form 53 key features. These 53 key features are inputted into the Lightweight Gradient Boosting Machine (LightGBM) model to predict plant resistance proteins. Experimental results of five-fold cross-validation on real datasets demonstrate that our proposed prediction method outperforms existing methods overall in accuracy, sensitivity, specificity, Matthews correlation coefficient, F1 score, and area under the curve (AUC) in the case of slightly imbalanced datasets. This research work will aid in filtrating plant resistance genes and proteins, and promote disease-resistant breeding for plants.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：广西农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台