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Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks
In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an “ensemble of singl...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234235/ https://www.ncbi.nlm.nih.gov/pubmed/35769876 http://dx.doi.org/10.1016/j.isci.2022.104553 |