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

Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic cancer cases, to assess metastatic risk...

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Detalles Bibliográficos
Autores principales: Jiang, Biaobin, Mu, Quanhua, Qiu, Fufang, Li, Xuefeng, Xu, Weiqi, Yu, Jun, Fu, Weilun, Cao, Yong, Wang, Jiguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602327/
https://www.ncbi.nlm.nih.gov/pubmed/34795255
http://dx.doi.org/10.1038/s41467-021-27017-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602327/
https://www.ncbi.nlm.nih.gov/pubmed/34795255
http://dx.doi.org/10.1038/s41467-021-27017-w

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