MFMGP : an integrated machine learning fusion model for genomic prediction

作者：Chaopu Zhang, Qiqi Liang, Yuye Yu, Shaojuan Jin, Shaojuan Jin, Jinmei Huang, Zhongping Xu, Erbao Liu, Wensheng Wang, Fan Zhang, Fangzhou Liu, Yingyao Shi, Fenge Li, Zhikang Li, Shuangxia Jin, Shuangxia Jin, Min Li · 发表于：Plant Biotechnology Journal · 年份：2025 · DOI：10.1111/pbi.14532 · 被引用次数：6 · 研究领域：Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals、Gene expression and cancer classification

Genome-wide selection (GS) represents a contemporary methodology that harnesses a comprehensive array of molecular markers across the entire genome. However, challenges such as lack of informative molecular markers and selection of appropriate and efficient GS model(s) have confined most GS-based breeding efforts to the realm of laboratory simulations (Wang et al., 2023). Compared to the conventional prediction models, the machine learning (ML) algorithm provides new insights for solving challenges such as big data analysis and high-performance parallel computing. GS using ML also has some limitations at the current stage such as limitations in model selection. Here, the MFMGP software is a fusion model that is based on a variety of ML training methods. The normalization fusion method with exponential decay weights involves assigning weights to the prediction results of each model and applying the exponential decay to these weights, so that more recent and/or more relevant model predictions have higher weights. Then, a weighted average of the model's prediction results is calculated to obtain the final fusion prediction by normalizing these weights (Figure 1a). The software of MFMGP for interactive GS analyses was made available at website: http://www.biohuaxing.com/#/MFMGP. To verify the prediction accuracy of the MFMGP model, we compared MFMGP with seven commonly used GS models. These included the classical GS model (GBLUP), four ML-based models (LightGBM, SVR, XGBoost and ...