Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells - Mcinga, Kuhle (Department of Integrative Biomedical Sciences (IBMS), 2025)
The emergence of pharmacogenomics databases has presented unique opportunities to leverage machine learning in precision medicine, particularly in drug response prediction. In this thesis, an in-depth investigation is conducted on carefully curated and integrated breast cancer focused datasets from the GDSC (Genomics of Drug Sensitivity in Cancer) and Achilles (CRISPR derived) project databases. Specifically, machine learning techniques are employed to accurately predict the drug responses of cancer cells, laying the groundwork for personalised treatment strategies. Through rigorous training of machine learning models, drug-response classifiers were devised that demonstrated remarkable predictive capabilities, with the best performing classifier achieving an F1-score of 0.86 and an AUC of 0.85, indicating its effectiveness in drug response prediction. Training these models on GDSC and Achilles datasets encompassing various drug IC50 values, ensured generalization of the models across different drugs and cell