AI-Based Cancer Drug Response Prediction: A Machine Learning Ensemble for Lung Cancer Pharmacogenomic Classification

Effectiveness of anticancer drug varies among various types of cancers so understanding their response towards these medications is essential. The paper describes an algorithm to classify drug response patterns from the lung cancer subset of GDSC2 data set through machine learning techniques. Continuous LN_IC50 response values are classified as sensitive and resistant based on predetermined cut-off points for creating a balanced dataset for modeling purposes. Eight out of nine numbers are obtained by drugs, cells lines targets pathways related responses while selecting them via selectkbest method through ANOVA Ftest. Logistic Regression, Support Vector Machine, Random Forest and XGBoost models were trained and evaluated on a held-out test set of 6,404 samples from an unbalanced dataset containing 32,018 records. Test accuracy of these models was between 92.61% and 92.96%, whereas their ROC-AUC value ranged from 0.978 to 0.981. Integrated with a Django based research tool by means of an algorithm that combines predictions via vote for producing outputs from this system. This device is meant solely to serve as an educational tool; it cannot be used by patients for self-diagnosis, healthcare decisions, or prescription of medications.