Alabi Waheed BANJOKO
@unilorin.edu.ng
Lecturer, Faculty of Physical Sciences
Lecturer, Faculty of Physical Sciences
University of Ilorin
RESEARCH, TEACHING, or OTHER INTERESTS
Statistics and Probability, Modeling and Simulation, Multidisciplinary, Applied Mathematics
Scopus Publications
Scopus Publications
Alabi W. Banjoko, Tiza Ng’uni, Nitalia Naidoo, Veron Ramsuran, Ollivier Hyrien, and Zaza M. Ndhlovu
Springer Science and Business Media LLC
Abstract Africa, being one of the most genetically diverse regions in the world, remains significantly underrepresented in high-resolution Human Leukocyte Antigen (HLA) data. The extensive genetic variation in HLA alleles across the region underscores the need for population-specific immunogenetic data to guide T-cell vaccine development. This study analysed Class I HLA data from Eastern and Southern African populations to assess regional genetic diversity. Analyses included allele and haplotype frequency distributions, deviations from Hardy–Weinberg equilibrium, linkage disequilibrium, and homozygosity test of neutrality across various populations. To further contextualise African HLA diversity, comparisons were made among African populations and also with African American and European American populations using the Hellinger diversity index and multidimensional scaling methods. The results revealed that South African populations exhibited an estimated average of 34.1% genetic diversity with respect to other African populations. Rwanda demonstrated an estimated 26.9% genetic diversity, Kenya (26.5%), Zambia (26.5%), and Uganda (24.7%). Additionally, in-country analyses revealed variations in HLA diversity among different tribes within each country. The estimated average in-country diversity was 51% in Kenya, 35.8% in Uganda, and 33.2% in Zambia. These results reveal various levels of genetic diversity among African populations. The highlighted differences in HLA Class I allele frequencies between Eastern and Southern African populations compared to US populations, demonstrate that it is inappropriate to extrapolate HLA data from US populations including that of African Americans when designing T-cell-inducing vaccines tailored to African populations. Our findings underscore the urgent need to generate high-resolution HLA data to guide vaccine development tailored to African populations.
Nitalia Naidoo, Tiza L. Ng'uni, Uvedhna Padia, Alabi W. Banjoko, and Zaza M. Ndhlovu
Wiley
ABSTRACTE*01:152 differs from E*01:03:05:01 by a non‐synonymous A>G substitution at gDNA position 958 (exon 3).
Alabi W. Banjoko, Waheed B. Yahya, and Oyebayo R. Olaniran
Elsevier BV
Kazeem A. Dauda, Kabir O. Olorede, Alabi W. Banjoko, Waheed B. Yahya, and Yusuf O. Ayipo
CRC Press
Alabi Waheed Banjoko, , Kawthar Opeyemi Abdulazeez, and
Penerbit Universiti Sains Malaysia
Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.
A W Banjoko, W B Yahya, M K Garba, and K O Abdulazeez
IOP Publishing
Abstract This paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.
A. W. Banjoko, W. B. Yahya, and M. K. Garba
Knowledge E
Background & Aim: In this study, efficient Support Vector Machine (SVM) algorithm for feature selection and classification of multi-category tumour classes of biological samples using gene expression profiles was proposed.
 Methods: Feature selection interface of the algorithm employed the F-statistic of the ANOVA–like testing scheme at some chosen family-wise-error-rate which ensured efficient detection of false-positive genes. The selected gene subsets using the above method were further screened for optimality using the Misclassification Error Rates yielded by each of them and their combinations in a sequential selection manner. In a 10-fold cross-validation, the optimal values of the SVM parameters with appropriate kernel were determined for tissue sample classification using one-versus-all approach. The entire data matrix was randomly partitioned into 95% training set to train the SVM classifier and 5% test set to evaluate the predictive performance of the classifier over 1,000 Monte-Carlo cross-validation runs. Published microarray breast cancer dataset with five clinical endpoints was employed to validate the results from the simulation studies.
 Results: Results from Monte-Carlo study showed excellent performance of the SVM classifier with higher prediction accuracy of the tissue samples based on the few gene biomarkers selected by the proposed feature selection method.
 Conclusion: SVM could be considered as a classification of multi-category tumour classes of biological