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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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2017
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/734bffa0c8884685a89d36929e58ac7d |
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