Cargando…
Predicting tumor cell line response to drug pairs with deep learning
BACKGROUND: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. RESULTS: We present a computational model for predicting cell line response to a subset of d...
Autores principales: | Xia, Fangfang, Shukla, Maulik, Brettin, Thomas, Garcia-Cardona, Cristina, Cohn, Judith, Allen, Jonathan E., Maslov, Sergei, Holbeck, Susan L., Doroshow, James H., Evrard, Yvonne A., Stahlberg, Eric A., Stevens, Rick L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302446/ https://www.ncbi.nlm.nih.gov/pubmed/30577754 http://dx.doi.org/10.1186/s12859-018-2509-3 |
Ejemplares similares
-
Converting tabular data into images for deep learning with convolutional neural networks
por: Zhu, Yitan, et al.
Publicado: (2021) -
Publisher Correction: Converting tabular data into images for deep learning with convolutional neural networks
por: Zhu, Yitan, et al.
Publicado: (2021) -
A cross-study analysis of drug response prediction in cancer cell lines
por: Xia, Fangfang, et al.
Publicado: (2021) -
Learning curves for drug response prediction in cancer cell lines
por: Partin, Alexander, et al.
Publicado: (2021) -
Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models
por: Zhu, Yitan, et al.
Publicado: (2020)