| Autor: |
Simon Höllerer, Laetitia Papaxanthos, Anja Cathrin Gumpinger, Katrin Fischer, Christian Beisel, Karsten Borgwardt, Yaakov Benenson, Markus Jeschek |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-020-17222-4 |
| Popis: |
Current methods to generate sequence-function data at large scale are either technically complex or limited to specific applications. Here the authors introduce DNA-based phenotypic recording to overcome these limitations and enable deep learning for accurate prediction of function from sequence. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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