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Updated: Jun 14, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

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GPS:利用数据融合策略来提高基于机器学习的基因组和表型选择的准确性

Hongshan Wu1, Shichao Jin1, Chao Xiang2

  • 1State Key Laboratory of Crop Genetics and Germplasm Enhancement, Zhongshan Biological Breeding Laboratory, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Jiangsu Key Laboratory of Soybean Biotechnology and Intelligent Breeding, Engineering Research Center of Plant Phenotyping, Ministry of Education, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China.

Plant communications
|June 13, 2025
PubMed
概括

一个新的基因组和表型选择 (GPS) 框架通过融合基因组和表型数据来提高植物育种的准确性. 数据融合策略显著提高了复杂特征的预测性能,稳定性和可转移性.

关键词:
农作物 农作物 农作物跨环境的预测预测.数据融合框架数据融合框架基因组选择 基因组选择现型选择 现型选择

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