Designing combination therapies with modeling chaperoned machine learning.
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a com...
Guardado en:
Autores principales: | Yin Zhang, Julie M Huynh, Guan-Sheng Liu, Richard Ballweg, Kayenat S Aryeh, Andrew L Paek, Tongli Zhang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f501b1749c324b698edad8e4e09907c9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A dynamical framework for complex fractional killing
por: Richard Ballweg, et al.
Publicado: (2017) -
Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques
por: Katsaros Konstantinos P., et al.
Publicado: (2021) -
Creating Unbiased Machine Learning Models by Design
por: Joseph L. Breeden, et al.
Publicado: (2021) -
Machine learning for perovskite materials design and discovery
por: Qiuling Tao, et al.
Publicado: (2021) -
Machine learning based CRISPR gRNA design for therapeutic exon skipping.
por: Wilson Louie, et al.
Publicado: (2021)