A predictive approach to genome design for multi-trait improvement and environmental adaptability via machine learning
- DOI
- 10.1007/s00122-026-05416-1
- Published
- 2026-10-25
- Container
- Theoretical and Applied Genetics
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s00122-026-05416-1,
title = {A predictive approach to genome design for multi-trait improvement and environmental adaptability via machine learning},
author = {Xu Han and Haoliang Yan and Ke Liu and Juwu Gong and Wankui Gong and Panhong Dai and Matthew Tom Harrison and Bin Wang and Yang Li and Jingtao Pan and Xu Gao and Yuzhen Shi and Youlu Yuan and Haihong Shang},
year = {2026},
journal = {Theoretical and Applied Genetics},
doi = {10.1007/s00122-026-05416-1},
url = {https://doi.org/10.1007/s00122-026-05416-1}
}RIS
TY - JOUR TI - A predictive approach to genome design for multi-trait improvement and environmental adaptability via machine learning AU - Xu Han AU - Haoliang Yan AU - Ke Liu AU - Juwu Gong AU - Wankui Gong AU - Panhong Dai AU - Matthew Tom Harrison AU - Bin Wang AU - Yang Li AU - Jingtao Pan AU - Xu Gao AU - Yuzhen Shi AU - Youlu Yuan AU - Haihong Shang PY - 2026 JO - Theoretical and Applied Genetics DO - 10.1007/s00122-026-05416-1 UR - https://doi.org/10.1007/s00122-026-05416-1 ER -
APA
Han, X., Yan, H., Liu, K., Gong, J., Gong, W., Dai, P., Harrison, M. T., Wang, B., Li, Y., Pan, J., Gao, X., Shi, Y., Yuan, Y., & Shang, H. (2026). A predictive approach to genome design for multi-trait improvement and environmental adaptability via machine learning. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-026-05416-1
Source records
- crossref · retrieved 2026-11-10T21:30:45.923Z