| Autor: |
Alex Wells, David Heckerman, Ali Torkamani, Li Yin, Jonathan Sebat, Bing Ren, Amalio Telenti, Julia di Iulio |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 10, Iss 1, Pp 1-9 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-019-13212-3 |
| Popis: |
Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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