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Item DEVELOPMENT OF A GENE FUSION DETECTION VALIDATION FRAMEWORK FOR LONG-READ RNA SEQUENCING USING ALIGNMENT EVIDENCE AND MACHINE LEARNING(Covenant University Ota, 2025-08) AMOS, IISOMINEA ISOZO; Covenant University DissertationGene fusions are critical drivers of cancer and serve as diagnostic and therapeutic biomarkers. Detecting them reliably from long-read RNA sequencing (RNA-Seq) data remains challenging due to high error rates and complex transcript structures. Current methods often depend on matched whole-genome sequencing (WGS) data, which may be unavailable or uninformative when fusions are expressed without clear genomic breakpoints. To address this, a long-read fusion validation pipeline was developed, optimized for transcript-level evidence by removing reliance on genomic data and focusing on functionally expressed fusions. The pipeline integrates alignment support from realigned soft-clipped reads, supplementary alignments, and full-length chimeric reads to validate transcripts. A Random Forest model was further trained using features derived from validated events to refine classification. Applied to five cancer cell line datasets, with emphasis on breast cancer, the pipeline achieved a 68.1% overall validation rate and 77.8% in MCF7. It distinguished true fusions, deprioritized database-reported false positives, and highlighted high-confidence novel candidates. Known fusions such as BCAS4–BCAS3 were confirmed, while MOV10–RHOC emerged as a biologically relevant novel fusion supported by multiple evidence types and recurrent in MCF7 and K562. Another candidate, CLUL1–TYMS, detected across four lines, likely represents a transcriptional read-through.Benchmarking against experimentally validated fusion transcripts, rather than DNA-based tools, established a transcript-focused alternative for fusion discovery. This dataset will be made publicly available to support benchmarking and machine learning research. The framework enables high-confidence detection of transcript-level fusions in cancer and shows strong potential for biomarker discovery and precision oncology.Item A COMPUTATIONAL FRAMEWORK FOR PREDICTING COMPOUNDPROTEIN INTERACTION FOR PROSTATE CANCER THERAPEUTIC DISCOVERY(Covenant University Ota, 2025-08) AGBI, Mayowa; Covenant University DissertationProstate cancer (PCa) is a major public health issue globally. In sub-Saharan Africa, with its limited number of diagnostic and treatment resources, it accounts for high mortality. The conventional approach to drug discovery is lengthy, expensive, and often insufficient to address the complex treatment-resistant prostate cancers present. In this study, a deep learning computational framework to predict Compound-Protein Interactions (CPI) for prostate cancer drug discovery was developed. An end-to-end machine learning pipeline was implemented using curated datasets from Zenodo, ChEMBL, BindingDB, and UniProt. Molecular representations for compounds were constructed using 2048-bit Morgan fingerprints, dimensionally reduced to 200 via Principal Component Analysis (PCA), and for the proteins, 100-dimensional 3-mer Word2Vec embeddings were used. These features were fed into a double-input deep neural network that was optimized with binary-cross-entropy loss, the Adam optimizer, and dropout regularization. The model identified five novel bioactive compounds for targeting proteins of prostate cancer biomarkers. Model confidence was used to prioritize predicted interactions for AR, SRC, and EGFR. Molecular docking in PyRx and AutoDock Vina, followed by visualization in Discovery Studio supporting strong binding affinity (-7.2 to -10) and complementarity from the structural point of view, constituting therapeutic potential. An integration of molecular docking enriched translational value to the prediction. The results presented here point to a disease-specific platform for in silico drug discovery in prostate cancer. This study opens a very promising path toward giving priority to candidate compounds by coupling the deep learning with structure-based affirmation. It provides a very viable ground to be merged with experimental validation and combinatorial therapy design, thereby taking one step further into machine learning-assisted precision oncology.Item ASSESSMENT OF FGFR2 AND FGFR4 POLYMORPHISMS IN NIGERIAN BREAST CANCER PATIENTS(Covenant University Ota, 2025-09) OGBODO, Peace Nzubechukwu; Covenant University DissertationBreast cancer (BC) persists as the most frequently occurring cancer in females, with a growing incidence percentage in sub-Saharan Africa. BC has been correlated with FGFR2 and FGFR4 genetic variations in different populations. However, the data on Nigerian women are scarce. This study investigated the association of FGFR2 rs1219648 (A>G), FGFR2 rs2981582 (A>G), and FGFR4 rs351855 (G>A) with BC risk in a Nigerian cohort. A case-control design was employed involving 75 BC cases and 75 controls. Using blood samples, genomic DNA was extracted, and SNP genotyping was conducted with the use of TaqMan® allelic discrimination assay. Genotype and allele frequencies comparison was conducted using chi-square, odds ratios, and Fisher’s exact tests. The FGFR2 rs1219648 G allele was significantly more common (48.0%) in cases than controls (35.3%), with the GG genotype conferring a significant increase in risk (OR = 2.61, 95% CI: 1.07 - 6.64, p = 0.039). FGFR2 rs2981582 showed no significant genotype-level association, but the minor A allele was more common in cases (43.2%) than controls (31.3%) (p = 0.045). FGFR4 rs351855 was not significantly associated with BC. None of the SNPs showed association with tumour immunohistochemical subtypes. The findings identify FGFR2 rs1219648 as a significant risk factor for BC in Nigerian women and highlight the need for larger, multi-centre studies to validate these associations.Item IMPACT OF SELECTED ESSENTIAL OILS AND PIPERONYL BUTOXIDE ON PYRETHROID RESISTANCE IN Anopheles gambiae IN OTA(Covenant University Ota, 2025-09) JEGEDE, Precious Osekafore; Covenant University DissertationMalaria remains a major public health challenge in Nigeria, with rising pyrethroid resistance in Anopheles gambiae undermining vector control strategies. Resistance is largely driven by detoxification enzymes and target-site mutations. Although piperonyl butoxide (PBO) is widely used as an insecticide synergist, its environmental and health risks highlight the need for safer alternatives. This study evaluated the potential of basil and geranium essential oils to inhibit detoxification enzymes, enhance permethrin efficacy, and compared their effects to PBO in An. gambiae. Adult females were collected, morphologically identified, and allocated into treatment groups for WHO susceptibility bioassays, enzyme activity assays, and allele-specific PCR (AS-PCR) for kdr mutation detection. Mosquitoes were exposed to permethrin alone (0.75%), basil or geranium essential oils at 1 (10 μL/mL), 5 (50 μL/mL), and 10% (100 μL/mL) v/v, or sub-lethal synergist assays combining permethrin (0.75%) with basil (1%), geranium (1%), or PBO (4%). Permethrin alone produced 25% mortality, confirming resistance according to WHO criteria. As separate insecticides, basil oil induced 0, 90, and 100% mortality at 10, 50, and 100 μL/mL, respectively, while geranium oil induced 10, 100, and 100% mortality at the same concentrations. In synergist assays, basil + permethrin achieved 25% mortality, geranium + permethrin 55%, and PBO + permethrin 60%. Enzyme assays showed no significant variation in GST activity, whereas cytochrome P450 activity was significantly elevated in permethrin-only treatments (p < 0.05) but remained near-control levels with basil, geranium, and PBO. AS-PCR detected a high frequency of the kdr-west L1014F allele (R = 0.76), with most mosquitoes homozygous resistant (RR). These findings confirm strong pyrethroid resistance in An. gambiae from Ota and highlight geranium oil and basil oil, as promising environmentally friendly insecticides and synergists for malaria vector control.Item COMPARATIVE EXPRESSION PROFILING OF SELECTED GLUTATHIONE S-TRANSFERASE GENES IN BLOOD-FED AND DELTAMETHRIN-EXPOSED Anopheles gambiae(Covenant University Ota, 2025-09) FOLAMADE, Joshua Kayode; Covenant University DissertationMalaria remains a leading public health challenge in sub-Saharan Africa, with Nigeria contributing the highest global burden. Anopheles gambiae is the major vector of this disease in Nigeria. Vector control strategies rely heavily on pyrethroid-based tools such as long-lasting insecticidal nets and indoor residual spraying. Metabolic resistance mediated by glutathione S-transferases (GSTs), particularly GSTe2, GSTe3, and GSTMS3, has been implicated in pyrethroid detoxification. Meanwhile, blood feeding induces profound physiological and molecular changes in mosquitoes, including alterations in detoxification pathways, suggesting a potential interaction with insecticide resistance. This study investigated how blood feeding and deltamethrin exposure influence the expression of GSTe2, GSTe3, and GSTMS3 in An. gambiae from Ota, Ogun State, Nigeria. Mosquitoes were reared from field-collected larvae and assigned to four experimental groups: blood-fed + deltamethrin exposed, blood-fed only, sugar-fed + deltamethrin exposed, and sugar-fed only (control). Susceptibility to deltamethrin was assessed using WHO bioassays and gene expression was quantified by qPCR. Results showed that blood-fed mosquitoes were significantly more susceptible to deltamethrin than sugar-fed counterparts, with higher mortality and faster knockdown times. At the molecular level, GSTe2 expression was generally down-regulated following deltamethrin exposure, while GSTe3 and GSTMS3 exhibited variable responses depending on feeding status. It was observed that blood feeding was the most consistent factor influencing GST expression, with insecticide exposure exerting context-dependent effects. These findings highlight that blood feeding modulates detoxification gene expression and susceptibility outcomes in An. gambiae, which implies dynamic physiological influences on resistance phenotypes. By integrating ecological behavior with molecular resistance mechanisms, this study underscores the importance of accounting for feeding status in resistance monitoring and vector control strategies. Locally relevant data such as these are critical for guiding malaria control interventions in Nigeria’s high-burden regions.Item EFFECTS OF INTERLEUKIN- 6 MEDIATED INFLAMMATION ON TELOMERASE EXPRESSION IN MALARIA PATIENTS(Covenant University Ota, 2025-09) FIAMITIA, Carrin; Covenant University DissertationMalaria remains a major global health burden that mostly affects young children in the African region, which has been associated with cellular aging and immune system exhaustion that is potentially mediated through telomere shortening and altered telomerase activity. The influence of malaria on the catalytic subunit hTERT, and how it modulates telomerase expression, in relation to the proinflammatory cytokines such as interleukin-6 (IL-6) and interferon-gamma (IFN-γ) is yet to be established. This study, therefore, aimed to explore the relationship between IL-6, IFN-γ levels, and hTERT gene expression in individuals with malaria infection. Ethical approval was obtained from the Covenant Health Research Ethics Committee (CHREC) before commencement of the study. A total of 50 malaria-infected samples were collected from ACE-Medicare and Covenant University Medical Center. Plasma generated from venous blood samples (5 ml) was separated by centrifugation, collected, and stored at –80 °C for subsequent cytokine analysis. Total RNA was extracted for cDNA synthesis and RT-qPCR-based quantification of hTERT expression. RNA concentration, integrity, and purity were analyzed using a Nanodrop spectrophotometer. A portion of the plasma (100 μl) was used for cytokine analysis using human IL-6 and IFN-γ using ELISA technique. Interleukin-6 levels (17.47 ± 25.11 pg/ml) were significantly higher (p<0.05) in the case compared to the control group (0.54 ± 0.46 pg/ml). The interferon gamma levels (117.74 ± 51.62 pg/ml) in the case group showed no significant difference (p>0.05) compared to the control group (104.50 ± 55.23 pg/ml). The Ct values of the hTERT gene expression were 33.38±4.48 in malaria patients in Nigeria, which is a possible standard range. For the first time, this study reports hTERT gene expression levels in Nigerian malaria patients and IL-6 as potential biomarkers for monitoring malaria progression, thereby providing a valuable tool for precision malaria treatment in Nigeria.Item ASSOCIATION OF IL6, TNF-α and IL10 POLYMORPHISM WITH PROSTATE CANCER RISK AND SEVERITY IN NIGERIAN MEN(Covenant University Ota, 2025-09) ALEEM, Adeola Abibat; Covenant University DissertationOne of the critical health burdens in Nigeria is prostate cancer (PCa) with high risk and death, especially men of African indigene. Persistent inflammation is influenced by soluble molecules such as interleukin-6 (IL6), tumor necrosis factor-alpha (TNFα), and interleukin-10 (IL10), which influences PCa development and malignancy, with single nucleotide polymorphisms (SNPs) in these genes influencing disease susceptibility and severity. This study investigated the association of IL6 (rs1800795), TNFα (rs1800629), and IL10 (rs1800872) SNPs with PCa risk and severity in a Nigerian cohort comprising 75 PCa victims and 81 healthy controls. Genotype and allele frequencies were determined using TaqMan SNP genotyping, and associations with PCa risk and severity (assessed via Gleason scores) were analysed. The results showed no statistical relationship between the studied SNPs and PCa risk. Specifically, rs1800629 showed a predominance of the GG genotype (85.7% cases, 86.4% controls) with a low minor allele frequency (MAF) for the A allele (7.04% cases, 6.72% controls; OR = 0.96, 95% CI: 0.39–2.38, p = 0.930), and no correlation with Gleason scores (p = 0.58). For rs1800872, genotype frequencies (TT: 14.3% cases, 16.9% controls; TG: 50.0% cases, 58.4% controls; GG: 35.7% cases, 24.7% controls). The minor T allele was less frequent in cases (39.3%) than in controls (46.1%), suggesting a protective effect, though the difference is not statistically significant. No meaningful associations was observed with PCa risk and the genotypes (OR = 1.711, GG vs. TT, p = 0.301; OR = 1.017, TG vs. TT, p = 0.971) or with Gleason scores (p = 0.95). Notably, rs1800795 exhibited complete monomorphism (GG genotype in all subjects), precluding its analysis as a biomarker for PCa risk or severity. The lack of significant associations may be attributed to population-specific genetic profiles, particularly the monomorphism of rs1800795 and low MAF of rs1800629, as well as the limited sample size, which constrained statistical power. These findings show the importance of population-specific genetic studies, as allele frequencies and their disease associations vary across populations. Future research should involve larger cohorts, genome-wide association studies, and functional analyses to explore other IL-6 SNPs, gene-environment interactions, and novel PCa-associated variants in Nigerians, contributing to improved molecular epidemiology and potential biomarkers for early diagnosis and targeted therapies in African populations.Item CONSTRUCTING GENE REGULATORY NETWORKS FOR BREAST CANCER STEM CELLS USING SINGLE-CELL MULTI-OMICS(Covenant University Ota, 2025-08) UJOH, Treasure Ulonna; Covenant University DissertationBreast cancer mortality is primarily driven by metastasis and therapeutic relapse; processes strongly linked to breast cancer stem cells (BCSCs). These cells are believed to orchestrate tumor initiation, resistance, and recurrence through complex gene regulatory networks (GRNs) that remain poorly characterized. This study aimed to construct and compare GRNs of BCSCs and normal mammary stem cells (MaSCs) using a single-cell multi-omics framework. Datasets that were publicly available, containing single-cell RNA sequencing (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) profiles, were sourced from normal breast tissue, primary tumors, recurrent tumors, and BCSC-enriched mammospheres. The datasets were subjected to strict preprocessing that involved filtering, normalization, and quality control. The scGLUE model, which uses a graph neural network to integrate multiple omics including transcriptomic and epigenomic data into a single latent space while conserving biological identity, was used to integrate the datasets. pySCENIC pipeline was then used, which combines co-expression analysis, cis-regulatory motif enrichment, and pruning to reconstruct gene regulatory networks, and high-confidence regulons were generated for BCSCs and MaSCs. Comparative network analysis revealed extensive “regulatory rewiring” in BCSCs, with transcription factors such as JUNB, FOSB, LEF1, SOX4, and MAFB emerging as master regulators absent or significantly altered in normal stem cells. Functional enrichment of BCSC-exclusive targets highlighted pathways central to metastasis and recurrence, including extracellular matrix remodeling, adhesion, migration, and growth factor signaling. Disease ontology mapping further confirmed strong associations with invasive breast carcinoma and therapy resistance. Collectively, this study provides one of the first integrated single-cell GRN maps contrasting BCSCs with their normal counterparts, establishing mechanistic links between regulatory rewiring and cancer hallmarks. The identification of BCSC-specific master regulators offers promising therapeutic entry points for interventions aimed at eradicating the root drivers of breast cancer relapse and metastasis.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 DissertationMalaria 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 surveillanceItem IMPROVEMENT OF INFERENCE-TIME PREDICTION FOR SPEECH EMOTION RECOGNITION USING ITERATIVE kNN MAJORITY VOTING ON WavLM FEATURE EMBEDDINGS(Covenant University Ota, 2025-08) FALANA, John Oluwaseun; Covenant University DissertationThe prediction inconsistency and poor decision boundaries in high-dimensional embedding spaces limit the performance of Speech Emotion Recognition (SER) systems. This study proposes a post-processing framework that applies iterative k-Nearest Neighbors (kNN) majority voting to refine the output of a fine-tuned WavLM model without requiring retraining. Using the CREMA-D, an English dataset with 7,442 samples, embeddings were extracted and iteratively relabelled based on local neighborhood structure in the latent space. This refinement process enhanced label consistency and leveraged proximity-based corrections at inference time. Model performance was evaluated using standard SER metrics (accuracy and F1-score) and t-SNE visualization. Results show that repeated kNN refinement improves both classification accuracy and the clarity of decision boundaries, with a 1.87% improvement in F1 score from baseline compared to an improvement of 0.67% by the SCL+kNN approach from baseline. The approach is model-agnostic, efficient, and data-centric, offering a viable alternative to computationally expensive retraining. It highlights the value of embedding-space operations for improving SER reliability in real-world settings.