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Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells
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...
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| Format: | Thesis |
| Language: | English English |
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Department of Integrative Biomedical Sciences (IBMS)
2025
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| _version_ | 1867614057007153152 |
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| access_status_str | Open Access |
| author | Mcinga, Kuhle |
| author2 | Sinkala, Musalula |
| author_browse | Mcinga, Kuhle Sinkala, Musalula |
| author_facet | Sinkala, Musalula Mcinga, Kuhle |
| author_sort | Mcinga, Kuhle |
| collection | Thesis |
| description | 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 |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41066 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:45:59.127Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Integrative Biomedical Sciences (IBMS) |
| publisherStr | Department of Integrative Biomedical Sciences (IBMS) |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41066 Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells Mcinga, Kuhle Sinkala, Musalula Martin, Darren Medicine 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 2025-03-03T06:57:36Z 2025-03-03T06:57:36Z 2024 2025-03-03T06:55:07Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/41066 en eng application/pdf Department of Integrative Biomedical Sciences (IBMS) Faculty of Health Sciences University of Cape Town |
| spellingShingle | Medicine Mcinga, Kuhle Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells |
| thesis_degree_str | Master's |
| title | Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells |
| title_full | Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells |
| title_fullStr | Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells |
| title_full_unstemmed | Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells |
| title_short | Using machine learning to understand the link between gene essentiality, gene expression and the chemosensitivity of cancer cells |
| title_sort | using machine learning to understand the link between gene essentiality gene expression and the chemosensitivity of cancer cells |
| topic | Medicine |
| url | http://hdl.handle.net/11427/41066 |
| work_keys_str_mv | AT mcingakuhle usingmachinelearningtounderstandthelinkbetweengeneessentialitygeneexpressionandthechemosensitivityofcancercells |