PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics

文献类型： 外文期刊

第一作者： Tong, Kai

作者： Tong, Kai;Dai, Liangli;Liao, Yuxue;Li, Zhaoling;Chen, Xiaojing;Chen, Xiaojing;Yan, Shen;Wang, Ting;Wang, Ting

作者机构：

关键词： feature selection; genomic prediction; machine learning; rice breeding; SNP

期刊名称：GENES （ 影响因子：3.5； 五年影响因子：3.9 ）

ISSN：

年卷期： 2024 年 15 卷 5 期

页码：

收录情况： SCI

摘要： As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.

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