Zhao, Z;
Li, K;
Toumazou, C;
Kalofonou, M;
(2019)
A computational model for anti-cancer drug sensitivity prediction.
In:
Proceedings of 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS).
IEEE: Nara, Japan.
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Abstract
Various methods have been developed to build models for predicting drug response in cancer treatment based on patient data through machine learning algorithms. Drug prediction models can offer better patient data classification, optimising sensitivity identification in cancer therapy for suitable drugs. In this paper, a computational model based on Deep Neural Networks has been designed for prediction of anti-cancer drug response based on genetic expression data using publicly available drug profiling datasets from Cancer Cell Line Encyclopedia (CCLE). The model consists of several parts, including continuous drug response prediction, discretization and a drug sensitivity result output. Regularization and compression of neuron connections is also implemented to make the model compact and efficient, outperforming other widely used algorithms, such as elastic net (EN), random forest (RF), support vector regression (SVR) and simple artificial neural network (ANN) in sensitivity analysis and predictive accuracy.
| Type: | Proceedings paper |
|---|---|
| Title: | A computational model for anti-cancer drug sensitivity prediction |
| Event: | 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BIOCAS.2019.8919228 |
| Publisher version: | https://doi.org/10.1109/BIOCAS.2019.8919228 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Cancer treatment, Drug sensitivity prediction, Computational model, Deep Neural Network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10092189 |
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