Yoosefzadeh-Najafabadi, M.; Eskandari, M.; Torabi, S.; Torkamaneh, D.; Tulpan, D.; Rajcan, I.
Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. Int. J. Mol. Sci. 2022, 23, 5538.
https://doi.org/10.3390/ijms23105538
AMA Style
Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I.
Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. International Journal of Molecular Sciences. 2022; 23(10):5538.
https://doi.org/10.3390/ijms23105538
Chicago/Turabian Style
Yoosefzadeh-Najafabadi, Mohsen, Milad Eskandari, Sepideh Torabi, Davoud Torkamaneh, Dan Tulpan, and Istvan Rajcan.
2022. "Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components" International Journal of Molecular Sciences 23, no. 10: 5538.
https://doi.org/10.3390/ijms23105538
APA Style
Yoosefzadeh-Najafabadi, M., Eskandari, M., Torabi, S., Torkamaneh, D., Tulpan, D., & Rajcan, I.
(2022). Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. International Journal of Molecular Sciences, 23(10), 5538.
https://doi.org/10.3390/ijms23105538