Are we there yet? A machine learning architecture to predict organotropic metastases
Abstract Background & Aims Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage...
Guardado en:
Autores principales: | Michael Skaro, Marcus Hill, Yi Zhou, Shannon Quinn, Melissa B. Davis, Andrea Sboner, Mandi Murph, Jonathan Arnold |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
BMC
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/fa8ad0a1a41c47debabc6d1cf6c1cc5d |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Heterogeneity of BCSCs contributes to the metastatic organotropism of breast cancer
por: Cenzhu Wang, et al.
Publicado: (2021) -
Mechanisms, Diagnosis and Treatment of Bone Metastases
por: Jozef Ban, et al.
Publicado: (2021) -
An ancient, yet current, experience of deficient architecture: the case of Doge
por: A. Pérez Negrete, et al.
Publicado: (2021) -
Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
por: Biaobin Jiang, et al.
Publicado: (2021) -
Association of radiation dose intensity with overall survival in patients with distant metastases
por: Johnny Kao, et al.
Publicado: (2021)