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...

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Detalles Bibliográficos
Autores principales: Tavolara, Thomas E., Niazi, M. Khalid Khan, Ginese, Melanie, Piedra-Mora, Cesar, Gatti, Daniel M., Beamer, Gillian, Gurcan, Metin N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Research Paper
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

Internet

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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