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Author: search.filters.author.Covenant University DissertationSubject: search.filters.subject.Plasmodium falciparum

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    DEVELOPMENT OF A MULTI-LABEL CLASSIFIER FOR PREDICTING GENETIC MARKERS ASSOCIATED WITH MULTI-DRUG RESISTANCE IN Plasmodium falciparum STRAINS
    (Covenant University Ota, 2025-08) OGUNDIMU, Temitayo Ayomikun; Covenant University Dissertation
    Malaria is an infectious disease of global health importance caused by Plasmodium falciparum. It is highly complicated by parasite’s ability to gain resistance to multiple antimalarial drugs simultaneously, a phenomenon known as multidrug resistance (MDR). Single-label models only predict resistance to one drug at a time and as such would not capture these complex resistance patterns, limiting their utility for real-world surveillance. To bridge this gap, this study developed and evaluated four advanced multi-label classification models: Random Forest with Binary Relevance (RFDTBR), Ensemble of Classifier Chains (ECCJ48), Ensemble of Binary Relevance (EBRJ48), and a Backpropagation Neural Network (BPNN), using genomic and phenotypic data for five key antimalarials. Notably, RFDTBR and EBRJ48 outperformed others in predicting exact MDR profiles, while BPNN performed faster compared to the other models. Sulfadoxine-Pyrimethamine had the lowest performance across the models. Specific genomic features consistently emerged as key predictive factors across all models. These findings demonstrate the value of multi-label learning for comprehensive MDR prediction. Also, effective models and genomic regions were identified, warranting further investigation, thereby paving the way for improved resistance surveillance
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    GENOME-WIDE IDENTIFICATION OF SHORT TANDEM REPEATS ASSOCIATED WITH MULTI-DRUG RESISTANCE IN Plasmodium falciparum STRAINS
    (Covenant University Ota, 2025-08) EMMANUELLA EKURI MAMTUMAMBOH; Covenant University Dissertation
    Antimalarial drug resistance in Plasmodium falciparum threatens global malaria control, and while single nucleotide polymorphisms (SNPs) are well-studied, the role of short tandem repeats (STRs) remains underexplored. This study investigates the contribution of pathogenic STRs to drug resistance using STR genotypes from HipSTR, phenotypic resistance data, and machine learning models. Allele frequency analysis revealed consistently lower alternative allele frequencies in resistant strains across all 14 chromosomes, with strong selective signals on chromosomes 2, 3, 4, 8, and 13. Population differentiation analyses (PCA, FST) identified key resistance loci near PfKelch13 and plasmepsin 2/3, along with potential novel resistance regions. A logistic regression model trained on STR alleles achieved perfect classification (AUC = 1.00), demonstrating the strong predictive power of STRs in distinguishing resistant from sensitive parasites. Top STRs showed both known and novel associations with resistance, reinforcing the polygenic nature of antimalarial resistance. These findings establish STRs as important genetic markers for resistance surveillance and highlight their potential utility in guiding malaria treatment strategies.