Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors

The location and timing of metastasis are still fundamentally unpredictable. Here the authors present the Metastatic Network model, a machine learning framework that integrates clinical data and DNA alterations to predict the risk of metastasis to specific organs as well as clinical outcomes

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Autores principales:	Biaobin Jiang, Quanhua Mu, Fufang Qiu, Xuefeng Li, Weiqi Xu, Jun Yu, Weilun Fu, Yong Cao, Jiguang Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f1a63a56f3864a8bb3b89959ab118679
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spelling	oai:doaj.org-article:f1a63a56f3864a8bb3b89959ab1186792021-11-21T12:35:33ZMachine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors10.1038/s41467-021-27017-w2041-1723https://doaj.org/article/f1a63a56f3864a8bb3b89959ab1186792021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-27017-whttps://doaj.org/toc/2041-1723The location and timing of metastasis are still fundamentally unpredictable. Here the authors present the Metastatic Network model, a machine learning framework that integrates clinical data and DNA alterations to predict the risk of metastasis to specific organs as well as clinical outcomesBiaobin JiangQuanhua MuFufang QiuXuefeng LiWeiqi XuJun YuWeilun FuYong CaoJiguang WangNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Biaobin Jiang Quanhua Mu Fufang Qiu Xuefeng Li Weiqi Xu Jun Yu Weilun Fu Yong Cao Jiguang Wang Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
description	The location and timing of metastasis are still fundamentally unpredictable. Here the authors present the Metastatic Network model, a machine learning framework that integrates clinical data and DNA alterations to predict the risk of metastasis to specific organs as well as clinical outcomes
format	article
author	Biaobin Jiang Quanhua Mu Fufang Qiu Xuefeng Li Weiqi Xu Jun Yu Weilun Fu Yong Cao Jiguang Wang
author_facet	Biaobin Jiang Quanhua Mu Fufang Qiu Xuefeng Li Weiqi Xu Jun Yu Weilun Fu Yong Cao Jiguang Wang
author_sort	Biaobin Jiang
title	Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
title_short	Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
title_full	Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
title_fullStr	Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
title_full_unstemmed	Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
title_sort	machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f1a63a56f3864a8bb3b89959ab118679
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Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors

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