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Daniel Runcie

Showing results (11-20 of 14) with videos related to

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Trends in Plant Science|October 27, 2024
Machine learning algorithms translate big data into predictive breeding accuracyJosé Crossa, Osval A Montesinos-Lopez, Germano Costa-Neto, et al.
Trends in Plant Science|January 31, 2025
Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced softwareJosé Crossa, Johannes W R Martini, Paolo Vitale, et al.
Science (New York, N.Y.)|November 30, 2023
Two teosintes made modern maizeNing Yang, Yuebin Wang, Xiangguo Liu, et al.
Proceedings of the National Academy of Sciences of the United States of America|June 30, 2022
An adaptive teosinte <i>mexicana</i> introgression modulates phosphatidylcholine levels and is associated with maize flowering timeAllison C Barnes, Fausto Rodríguez-Zapata, Karla A Juárez-Núñez, et al.
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Showing results (11-20 of 14) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 14 results.
Trends in Plant Science|October 27, 2024
Machine learning algorithms translate big data into predictive breeding accuracyJosé Crossa, Osval A Montesinos-Lopez, Germano Costa-Neto, et al.
Trends in Plant Science|January 31, 2025
Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced softwareJosé Crossa, Johannes W R Martini, Paolo Vitale, et al.
Science (New York, N.Y.)|November 30, 2023
Two teosintes made modern maizeNing Yang, Yuebin Wang, Xiangguo Liu, et al.
Proceedings of the National Academy of Sciences of the United States of America|June 30, 2022
An adaptive teosinte <i>mexicana</i> introgression modulates phosphatidylcholine levels and is associated with maize flowering timeAllison C Barnes, Fausto Rodríguez-Zapata, Karla A Juárez-Núñez, et al.
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