Programme: Computer Science

Permanent URI for this collectionhttp://itsupport.cu.edu.ng:4000/handle/123456789/28782

Here you will find projects strictly related to Computer Science

Browse

Subject: search.filters.subject.Plasmodium falciparum

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    AN OPTIMIZED DEEP-FOREST MODEL USING A MODIFIED DIFFERENTIAL EVOLUTION OPTIMIZATION ALGORITHM: A CASE OF HOST-PATHOGEN PROTEIN-PROTEIN INTERACTION PREDICTION
    (Covenant University Ota, 2025-04) EMMANUEL JERRY DAUDA; Covenant University Thesis
    Deep forest is an advanced ensemble learning technique that employs forest structures within a cascade framework, leveraging deep architectures to enhance predictive performance by adaptively capturing high-level feature representations. Despite its promise, deep forest models often face critical challenges, including manual hyperparameter optimization and inefficiencies in computational time and memory usage. To address these limitations, Bayesian optimization, a prominent model-based hyperparameter optimization method, is frequently utilized, with Differential Evolution (DE) serving as the acquisition function in recent implementations. However, DE's reliance on random index selection for constructing donor vectors introduces inefficiencies, as suboptimal or redundant indices may hinder the search for optimal solutions. This study introduces an optimized deep forest algorithm that integrates a modified DE acquisition function into Bayesian optimization to improve host-pathogen protein-protein interaction (HPPPI) prediction. The modified DE approach incorporates a weighted and adaptive donor vector selection mechanism, enhancing the exploration and exploitation of hyperparameter configurations. Performance evaluations using 10-fold cross-validation on human–Plasmodium falciparum (PF) protein sequence datasets sourced from reputable databases demonstrated the model's superiority over traditional Bayesian optimization, genetic algorithms, evolutionary strategies, and conventional machine learning models. The optimized framework achieved an accuracy of 89.3%, sensitivity of 85.4%, precision of 91.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 89.1%, surpassing existing methods. Additionally, the model exhibited reduced computational time and memory usage. The optimized DF was deployed as a web-based pipeline, DFH3PI (Deep Forest Host-Pathogen Protein-Protein Interaction Prediction), which successfully identified three potential human–PF PPIs previously classified as non-interacting: P50250–P08319, Q8ILI6–O94813, and Q7KQL3–Q96GQ7. These findings not only present the potential of DFH3PI for advancing HPPPI prediction but also establish the optimized deep forest framework as a transformative tool in computational biology. Its ability to combine accuracy and efficiency marks a significant step forward in predictive modeling.