机器学习全基因组选择研究进展  

Research Progress in Machine Learning Genomic Selection

作　　者：李竟 张元旭 王泽昭 陈燕[2] 徐凌洋[2] 张路培[2] 高雪[2] 高会江[2] 李俊雅[2] 朱波[2] 郭鹏 LI Jing;ZHANG Yuanxu;WANG Zezhao;CHEN Yan;XU Lingyang;ZHANG Lupei;GAO Xue;GAO Huijiang;LI Junya;ZHU Bo;GUO Peng(College of Computer and Information Engineering,Tianjin Agricultural University,Tianjin 300384,China;Institute of Animal Science,Chinese Academy of Agricultural Sciences,Beijing 100193,China)

机构地区：[1]天津农学院计算机与信息工程学院,天津300384 [2]中国农业科学院北京畜牧兽医研究所,北京100193

出　　处：《畜牧兽医学报》2024年第6期2281-2292,共12页ACTA VETERINARIA ET ZOOTECHNICA SINICA

基　　金：国家自然科学基金(32272843)。

摘　　要：机器学习方法是全基因组选择研究的重要分支,深度学习是近年来机器学习领域新的研究热点。本文介绍了机器学习以及深度学习全基因组选择研究的原理和应用发展,分别从模型框架、模型参数、特征选择等方面对深度学习全基因组育种值估计研究进展进行了阐述,探讨了深度学习全基因组选择研究中面临的一些的问题,并对未来进行了展望。Machine learning method is an important branch of genomic selection,and deep learning has become a new research hotspot in the field of machine learning in recent years.The principles and application development of machine learning and deep learning genomic selection were introduced in this paper,and the research progress of deep learning genomic breeding value estimation from the aspects of model framework,model parameters,feature selection were elaborated.Some problems in deep learning genomic selection research were explored and prospects in the future were discussed.

关 键 词：全基因组选择 研究进展 机器学习 深度学习 原理与应用 

分 类 号：S813.1[农业科学—畜牧学]