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Machine learning for predicting phenotype from genotype and environment
Affiliation:1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China;2. Hubei Hongshan Laboratory, Wuhan 430070, China;3. USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA;4. Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA;1. The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Private Bag 92169, Auckland 1142, New Zealand;2. The New Zealand Institute for Plant and Food Research Limited, Food Industry Science Centre, Private Bag 11600, Palmerston North 4442, New Zealand;1. Shanghai Collaborative Innovation Center of Agri-Seeds, Joint Center for Single Cell Biology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China;2. Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 201602, China;1. School of Biological Science and Technology, University of Jinan, Jinan 250022, China;2. Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany;3. State Key Laboratory of Plant Physiology and Biochemistry and National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
Abstract:
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