A Deep Learning Framework for Predicting Response to Therapy in Cancer
Language English Country United States Media print
Document type Journal Article, Research Support, Non-U.S. Gov't
Grant support
15874
Cancer Research UK - United Kingdom
MC_PC_14112
Medical Research Council - United Kingdom
MC_UU_12022/2
Medical Research Council - United Kingdom
PubMed
31825821
DOI
10.1016/j.celrep.2019.11.017
PII: S2211-1247(19)31488-3
Knihovny.cz E-resources
- Keywords
- DNN, deep neural networks, drug response prediction, machine learning, precision medicine,
- MeSH
- Survival Analysis MeSH
- Drug Resistance, Neoplasm * MeSH
- Deep Learning * MeSH
- Precision Medicine methods MeSH
- Humans MeSH
- Cell Line, Tumor MeSH
- Neoplasms drug therapy genetics metabolism MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.
Applied Bioinformatics Laboratories NYU School of Medicine New York NY 10016 USA
Biomedical Research Foundation of the Academy of Athens 4 Soranou Ephessiou Str Athens 11527 Greece
School of Mechanical Engineering National Technical University of Athens Zografou 15780 Greece
References provided by Crossref.org
Johann Gregor Mendel: the victory of statistics over human imagination