A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome

Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a spatially informed, prognostic gene signature. The...

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Detalles Bibliográficos
Autores principales: Ing, Nathan, Huang, Fangjin, Conley, Andrew, You, Sungyong, Ma, Zhaoxuan, Klimov, Sergey, Ohe, Chisato, Yuan, Xiaopu, Amin, Mahul B., Figlin, Robert, Gertych, Arkadiusz, Knudsen, Beatrice S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643431/
https://www.ncbi.nlm.nih.gov/pubmed/29038551
http://dx.doi.org/10.1038/s41598-017-13196-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643431/
https://www.ncbi.nlm.nih.gov/pubmed/29038551
http://dx.doi.org/10.1038/s41598-017-13196-4

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