| Autor: |
Chia-Yi Cheng, Ying Li, Kranthi Varala, Jessica Bubert, Ji Huang, Grace J. Kim, Justin Halim, Jennifer Arp, Hung-Jui S. Shih, Grace Levinson, Seo Hyun Park, Ha Young Cho, Stephen P. Moose, Gloria M. Coruzzi |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-021-25893-w |
| Popis: |
Predicting complex phenotypes from genomic information is still a challenge. Here, the authors use an evolutionarily informed machine learning approach within and across species to predict genes affecting nitrogen utilization in crops, and show their approach is also useful in mammalian systems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink