4.5 Article

LightGBM: accelerated genomically designed crop breeding through ensemble learning

期刊

GENOME BIOLOGY
卷 22, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13059-021-02492-y

关键词

Genomic prediction; Genomic selection; Machine learning; Ensemble learning; Maize; Crop breeding; LightGBM; rrBLUP

资金

  1. National Science Foundation of China [31871706, 31525017]
  2. China Postdoctoral Science Foundation [2020TQ0355]
  3. National Key Research and Development Program of China [2018YFA0901003, 2016YFD0100803]

向作者/读者索取更多资源

LightGBM is an ensemble model of decision trees used for classification and regression prediction, showing superior performance in genomic selection-assisted breeding. Through benchmark tests, it demonstrates advantages in prediction precision, model stability, and computing efficiency.
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.

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