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• W4323316679 abstract "Genomic selection (GS), a strategy to use genotypes to predict phenotypes via statistical or machine learning models, has become a routine practice in plant breeding programs. GS can speed up the genetic gain by reducing phenotyping costs and/or shortening the breeding cycles. GS analysis is complicated involving data clean up and formatting, training and test population analysis, model selection and evaluation, and parameter optimization. In addition, GS analysis also requires some programming skills and knowledge of statistical modeling. Thus, we need a more practical GS tools for breeders. To alleviate this difficulty, we developed the web-based platform IP4GS (https://ngdc.cncb.ac.cn/ip4gs/), which offers a user-friendly interface to perform GS analysis simply through point-and-click actions. IP4GS currently includes seven commonly used models, eleven evaluation metrics, and visualization modules, offering great convenience for plant breeders with limited bioinformatics knowledge to apply GS analysis." @default.
• W4323316679 created "2023-03-07" @default.
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• W4323316679 date "2023-03-06" @default.
• W4323316679 modified "2023-09-26" @default.
• W4323316679 title "IP4GS: Bringing genomic selection analysis to breeders" @default.
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• W4323316679 doi "https://doi.org/10.3389/fpls.2023.1131493" @default.
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