Deep learning integrates histopathology and proteogenomics at a pan-cancer level

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types f...

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Detalles Bibliográficos
Autores principales: Wang, Joshua M., Hong, Runyu, Demicco, Elizabeth G., Tan, Jimin, Lazcano, Rossana, Moreira, Andre L., Li, Yize, Calinawan, Anna, Razavian, Narges, Schraink, Tobias, Gillette, Michael A., Omenn, Gilbert S., An, Eunkyung, Rodriguez, Henry, Tsirigos, Aristotelis, Ruggles, Kelly V., Ding, Li, Robles, Ana I., Mani, D.R., Rodland, Karin D., Lazar, Alexander J., Liu, Wenke, Fenyö, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518635/
https://www.ncbi.nlm.nih.gov/pubmed/37582371
http://dx.doi.org/10.1016/j.xcrm.2023.101173

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518635/
https://www.ncbi.nlm.nih.gov/pubmed/37582371
http://dx.doi.org/10.1016/j.xcrm.2023.101173

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