Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
European Bioinformatics Institute · Wellcome Trust · +3 more institutions
Abstract
Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified…
Citation impact
- FWCI
- 21.97
- Percentile
- 100%
- References
- 40
Authors
7- MPMichael P. Menden
European Bioinformatics Institute, Wellcome Trust
- FIFrancesco Iorio
Wellcome Trust, European Bioinformatics Institute, Wellcome Sanger Institute
- MJMathew J. Garnett
Wellcome Sanger Institute
- UMUltan McDermott
Wellcome Sanger Institute
- CHCyril H. Benes
Massachusetts General Hospital, Harvard University
Topics & keywords
- In silico
- Cancer drugs
- Computational biology
- Cancer cell lines
- Drug
- Drug response
- Drug discovery
- Computer science
- Good health and well-being