Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells
| dc.contributor.advisor | Sinkala, Musalula | |
| dc.contributor.advisor | Martin, Darren | |
| dc.contributor.author | Mcinga, Kuhle | |
| dc.date.accessioned | 2025-03-03T06:57:36Z | |
| dc.date.available | 2025-03-03T06:57:36Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2025-03-03T06:55:07Z | |
| dc.description.abstract | 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 | |
| dc.identifier.apacitation | Mcinga, K. (2024). <i>Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells</i>. (). University of Cape Town ,Faculty of Health Sciences ,Department of Integrative Biomedical Sciences (IBMS). Retrieved from http://hdl.handle.net/11427/41066 | en_ZA |
| dc.identifier.chicagocitation | Mcinga, Kuhle. <i>"Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells."</i> ., University of Cape Town ,Faculty of Health Sciences ,Department of Integrative Biomedical Sciences (IBMS), 2024. http://hdl.handle.net/11427/41066 | en_ZA |
| dc.identifier.citation | Mcinga, K. 2024. Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells. . University of Cape Town ,Faculty of Health Sciences ,Department of Integrative Biomedical Sciences (IBMS). http://hdl.handle.net/11427/41066 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Mcinga, Kuhle AB - 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 DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Medicine LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells TI - Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells UR - http://hdl.handle.net/11427/41066 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/41066 | |
| dc.identifier.vancouvercitation | Mcinga K. Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells. []. University of Cape Town ,Faculty of Health Sciences ,Department of Integrative Biomedical Sciences (IBMS), 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41066 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Integrative Biomedical Sciences (IBMS) | |
| dc.publisher.faculty | Faculty of Health Sciences | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | Medicine | |
| dc.title | Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |