Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach

Tansey, W., Li, K., Zhang, H., Linderman, S. W., Rabadan, R., Blei, D. M., & Wiggins, C. H. (2021). Dose–response modeling in high-throughput cancer drug screenings: an end-to-end approach. Biostatistics, 23(2), 643–665. https://doi.org/10.1093/biostatistics/kxaa047
Authors:
Wesley Tansey
Kathy Li
Haoran Zhang
Scott W. Linderman
Raul Rabadan
David M. Blei
Chris H. Wiggins
Affiliated Authors:
Kathy Li
Haoran Zhang
Scott W. Linderman
Raul Rabadan
David M. Blei
Chris H. Wiggins
Columbia Affiliation:
Author Keywords:
deep learning
dose-response modeling
drug discovery
empirical bayes
high-throughput screening
personalized medicine
elink
Publication Type:
Article
Unique ID:
10.1093/biostatistics/kxaa047
PMID:
33417699
DOI:
10.1093/biostatistics/kxaa047
Journal:
Biostatistics
Publication Date:
Data Source:
Scopus
Source Link:
View Scopus Record

Record Created: