FEATURE SELECTION FOR EFFICIENT EPISTASIS MODELING FOR PHENOTYPE PREDICTION

• 1. 一种基于木本植物叶片表型特征的净光合速率预测模型的构建方法和预测方法 [P] . 中国专利： CN108229065B . 2019.03.26
• 2. 基于DNA甲基化水平的木本植物叶片表型特征和光合特性预测模型的构建方法及预测方法 [P] . 中国专利： CN108319984B . 2019.07.02
• 3. FEATURE SELECTION FOR EFFICIENT EPISTASIS MODELING FOR PHENOTYPE PREDICTION [P] . 美国专利： US2019012427A1 . 2019-01-10

  机译： 用于表型预测的有效模型的特征选择

• 4. Feature selection for efficient epistasis modeling for phenotype prediction [P] . 美国专利： US10108775B2 . 2018-10-23

  机译： 用于表型预测的有效上位建模的特征选择

• 5. Feature selection for efficient epistasis modeling for phenotype prediction [P] . 美国专利： US10102333B2 . 2018-10-16

  机译： 用于表型预测的有效上位建模的特征选择

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• 6. Development of Efficient and Optimal Models for Software Maintainability Prediction using Feature Selection Techniques [C] . Kirti Lakra, Anuradha Chug International Conference on Computing for Sustainable Global Development . 2021
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• 8. Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE) [O] . Johannes W. R. Martini, Ning Gao, Diercles F. Cardoso, 2017
• 9. Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments [O] . Elaheh Vojgani, Torsten Pook, Johannes W. R. Martini, 2021

• 1. An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination [J] . Hakan Gunduz . 金融创新(英文) . 2021,第1期
• 2. A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation [J] . Jiemin Lin ,Haiming Li . 电脑和通信(英文) . 2021,第11期
• 3. Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection [J] . Favorisen Rosyking Lumbanraja ,Ngoc Giang Nguyen ,Dau Phan . 生物医学工程（英文） . 2018,第6期
• 4. 一种基于EEMD-特征选择-误差补偿混合方法的风速预测模型 [C] . 张超凡 ,姜言 ,黄国庆 . 第十八届全国结构风工程学术会议暨第四届全国风工程研究生论坛 . 2017
• 5. 特征选择与模型可解释性在代谢综合征风险预测中的应用研究 [A] . 张燕 . 2021