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Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning
BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic div...
Autores principales: | Tavolara, Thomas E., Niazi, M. Khalid Khan, Ginese, Melanie, Piedra-Mora, Cesar, Gatti, Daniel M., Beamer, Gillian, Gurcan, Metin N. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658666/ https://www.ncbi.nlm.nih.gov/pubmed/33166789 http://dx.doi.org/10.1016/j.ebiom.2020.103094 |
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