数字农科院2.0

An interpretable integrated machine learning framework for genomic selection

文献类型： 外文期刊

作者： Jinbu Wang;Jia Zhang;Wenjie Hao;Wencheng Zong;Mang Liang;Fuping Zhao;Longchao Zhang;Lixian Wang;Huijiang Gao;Ligang Wang

作者机构：

关键词： Dimensionality reduction;Genomic selection;Interpretability;Machine learning;Pig

期刊名称： Smart Agricultural Technology

ISSN： 2772-3755

年卷期： 2025 年 12 卷

页码：

收录情况： ESCI（2025版）

摘要： Although machine learning (ML) methods have shown growing promise for genomic selection (GS), several key challenges hinder their widespread application. In this study, we conducted a comprehensive analysis comparing the performance of various ML models, along with investigations into parameter optimization, dimensionality reduction, feature selection, and the “black box” problem. We also proposed an efficient and interpretable framework, NTLS (NuSVR + TPE + LightGBM + SHAP). In the prediction of Yorkshire pig populations, NTLS outperformed the genomic best linear unbiased prediction (GBLUP) model, achieving improvements in predictive accuracy of 5.1%, 3.4%, and 1.3% for days to 100 kg (DAYS), back fat at 100 kg (BF), and number of piglets born alive (NBA), respectively. Moreover, we introduced the NuSVR model, which achieved the highest accuracy among nine compared algorithms. Our findings further highlight the importance of interpretable learning in GS and provide a detailed multi-level application of the SHAP algorithm.

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