Research Progress of Machine Learning and Its Application in Animal Genetics and Breeding
With the progress of research on intelligent animal husbandry and the development of high-throughput omics platforms,animal genetic breeding has been gradually entered the"Breeding 3.0"in the era of big data.Machine learning is an indispensable and effective means of big data research.Machine learning,the automatic acquisition of knowledge by computers,is a multidisciplinary cross-discipline,involving many disciplines such as probability theory and statistics.As one of the popular algorithms in artificial intelligence and data mining,it has many advantages such as high learning rate,good generalization ability and high accuracy.It has been an important tool in the field of bioinformatics analysis for processing big data and making predictions.Nowadays,machine learning is widely used for the integration and analysis of genomic,transcriptomic,proteomic,metabolomic and other multi-omics data,especially in the computing of genomic estimated breeding value(GEBV),genotype imputation,the prediction of protein structure and function of livestock,and other outstanding achievements.Firstly,the author introduced the concepts and principles of several common machine learning algorithms.Besides,the outstanding research results achieved by machine learning algorithms in genetic breeding of important livestock(pigs,cattle,sheep,and goats)were reviewed and further discussed the advantages and disadvantages of certain machine learning algorithms as well as some of the problems in animal genetic breeding.Finally,the future development of machine learning was summarized and prospected,aiming to improve the accuracy and efficiency of estimation,accelerate the genetic progress of populations,and rapidly implement precision breeding.
machine learningbig dataanimal genetics and breedinggenetic improvement
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