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Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies
In conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. He...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496266/ https://www.ncbi.nlm.nih.gov/pubmed/34499126 http://dx.doi.org/10.1093/g3journal/jkab228 |