Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice

BACKGROUND: Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (mic...

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Detalles Bibliográficos
Autores principales: Tavolara, Thomas E., Niazi, M.K.K., Gower, Adam C., Ginese, Melanie, Beamer, Gillian, Gurcan, Metin N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138606/
https://www.ncbi.nlm.nih.gov/pubmed/34000621
http://dx.doi.org/10.1016/j.ebiom.2021.103388

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138606/
https://www.ncbi.nlm.nih.gov/pubmed/34000621
http://dx.doi.org/10.1016/j.ebiom.2021.103388

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