Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
Ho, Nyaga et al. develop a machine learning approach for ranking tissue-specific gene regulatory affects, used here for type 1 diabetes SNPs. They identify the lung as a site where these regulatory impacts can be most impactful, which may contribute to understanding the link between respiratory issu...
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| Auteurs principaux: | , , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/9509bda596664eab9816f6a97f9db45f |
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