文献类型： 外文期刊
作者： Gu, Lin-Lin 1 ; Yang, Run-Qing 3 ; Wang, Zhi-Yong 1 ; Jiang, Dan 1 ; Fang, Ming 1 ;
作者机构： 1.Jimei Univ, Key Lab Hlth Mariculture East China Sea, Minist Agr & Rural Affairs, Xiamen, Peoples R China
2.Jimei Univ, Fisheries Coll, Xiamen, Peoples R China
3.Chinese Acad Fishery Sci, Res Ctr Aquat Biotechnol, Beijing, Peoples R China
4.Heilongjiang Bayi Agr Univ, Life Sci Coll, Daqing, Peoples R China
关键词： Genomic prediction; Genomic selection; Ensemble learning; Machine learning
期刊名称：BMC BIOINFORMATICS （ 影响因子：3.0； 五年影响因子：4.3 ）
ISSN： 1471-2105
年卷期： 2024 年 25 卷 1 期
页码：
收录情况： SCI
摘要： Background Whole genome variants offer sufficient information for genetic prediction of human disease risk, and prediction of animal and plant breeding values. Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others.Results We herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesC pi, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison p-value of ELPGV over basic methods were varied from 4.853E-118 to 9.640E-20 for WTCCC dataset.Conclusions ELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.
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