Rizwan202007a (8).pdf (1.49 MB)
Download filePersonalized drug-response prediction model for lung cancer patients using machine learning
Lung cancer caused by mutations in the epidermal
growth factor receptor (EGFR) is a major cause of cancer deaths
worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been
developed, and have shown increased survival rates and quality of
life in clinical studies. However, drug resistance is a major issue,
and treatment efficacy is lost after about an year. Therefore,
predicting the response to targeted therapies for lung cancer
patients is a significant research problem. In this work, we
address this issue and propose a personalized model to predict the
drug-response of lung cancer patients. This model uses clinical
information, geometrical properties of the drug binding site,
and the binding free energy of the drug-protein complex. The
proposed model achieves state of the art performance with 97.5%
accuracy, 100% recall, 95% precision, and 96.3% F1-score with
a random forest classifier. This model can also be tested on other
types of cancer and diseases, and we believe that it may help
in taking optimal clinical decisions for treating patients with
targeted therapies
Funding
Hong Kong Research Grants Council (Project 11200818)
City University of Hong Kong (Project 9610034)
History
Email Address of Submitting Author
engr.rizwanqureshi786@gmail.comSubmitting Author's Institution
City University of Hong KongSubmitting Author's Country
- Hong Kong