AKINWALE, Mercy Ojochenwumi and Covenant University, Theses Masters (2024) GENAPP: A WEB APPLICATION FOR PREDICTING PLASMODIUM FALCIPARUM RESISTANCE TO SELECTED ANTIMALARIA DRUGS. Masters thesis, Covenant University.
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Abstract
Antimalarial drug resistance poses a significant challenge to global malaria control efforts, particularly in regions burdened by Plasmodium falciparum, the deadliest malaria parasite. The development and spread of resistance to widely used antimalarial drugs, such as chloroquine, Lumefantrine, Halofantrine, Quinine, Piperaquine and Dihydroartemisinin, have greatly impacted treatment efficacy and disease outcomes. This resistance is driven by various genetic mutations in P. falciparum, which confer the ability to survive drug exposure. This study explores the prediction of antimalarial drug resistance using machine learning algorithms Random Forest, Gradient Boosting Machine (GBM), and Support Vector Machine (SVM). Focusing on six key antimalarial drugs Chloroquine, Dihydroartemisinin, Lumefantrine, Quinine, Halofantrine, and Piperaquine the research aims to identify genetic markers that contribute to resistance and develop predictive models to enhance treatment strategies. To avoid model overfitting, 5-fold cross-validation was conducted on the training set to choose the optimal hyperparameter values. Regardless of the resistance mechanism, whether acquired resistance or point mutations in the chromosome, the accuracy (mean cross-validation score) of Random Forest had an average of 83% across all drugs. The model significantly classified the resistant isolates from the sensitive isolates of the parasite and could be used as potential tools in antimalarial resistance surveillance and clinical studies. A number of genes associated with antimalaria drug resistance were identified. Novel genes and loci were also discovered, of interest are genes on chromosomes 1, 4, 7, 8, 9, 10, 11, 17 and 19.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Machine learning, Antimalarial drug resistance, Plasmodium falciparum, genomic studies, phenotype prediction, malaria eradication |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | nwokealisi |
| Date Deposited: | 25 Sep 2024 13:30 |
| Last Modified: | 25 Sep 2024 13:30 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18456 |
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