| Autor: |
Xi Xiang, Giulia I. Corsi, Christian Anthon, Kunli Qu, Xiaoguang Pan, Xue Liang, Peng Han, Zhanying Dong, Lijun Liu, Jiayan Zhong, Tao Ma, Jinbao Wang, Xiuqing Zhang, Hui Jiang, Fengping Xu, Xin Liu, Xun Xu, Jian Wang, Huanming Yang, Lars Bolund, George M. Church, Lin Lin, Jan Gorodkin, Yonglun Luo |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-021-23576-0 |
| Popis: |
High-quality gRNA activity data is needed for accurate on-target efficiency predictions. Here the authors generate activity data for over 10,000 gRNA and build a deep learning model CRISPRon for improved performance predictions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink