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

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

Abstract 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 signa...

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Bibliographic Details
Main Authors: Nathan Ing, Fangjin Huang, Andrew Conley, Sungyong You, Zhaoxuan Ma, Sergey Klimov, Chisato Ohe, Xiaopu Yuan, Mahul B. Amin, Robert Figlin, Arkadiusz Gertych, Beatrice S. Knudsen
Format: Article
Language:English
Published: Nature Publishing Group 2017-10-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-017-13196-4

Internet

https://doi.org/10.1038/s41598-017-13196-4

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