Alabi Waheed BANJOKO

@unilorin.edu.ng

Lecturer, Faculty of Physical Sciences
Lecturer, Faculty of Physical Sciences
University of Ilorin

Alabi Waheed BANJOKO


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https://researchid.co/awb2023

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics and Probability, Modeling and Simulation, Multidisciplinary, Applied Mathematics
8

Scopus Publications

129

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • High resolution class I HLA-A, -B, and -C diversity in Eastern and Southern African populations
    Alabi W. Banjoko, Tiza Ng’uni, Nitalia Naidoo, Veron Ramsuran, Ollivier Hyrien, et al.
    Scientific Reports, 2025
    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.
  • The Novel HLA-E*01:152 Allele Identified in African Populations Using Next-Generation Sequencing
    Nitalia Naidoo, Tiza L. Ng'uni, Uvedhna Padia, Alabi W. Banjoko, Zaza M. Ndhlovu
    Hla, 2025
    E*01:152 differs from E*01:03:05:01 by a non‐synonymous A>G substitution at gDNA position 958 (exon 3).
  • A multi-objective optimization algorithm for gene selection and classification in cancer study
    Alabi W. Banjoko, Waheed B. Yahya, Oyebayo R. Olaniran
    Applied Soft Computing, 2025
  • Genetic Diagnosis, Classification, and Risk Prediction in Cancer Using Next-Generation Sequencing in Oncology
    Kazeem A. Dauda, Kabir O. Olorede, Alabi W. Banjoko, Waheed B. Yahya, Yusuf O. Ayipo
    Computational Approaches in Bioengineering Volume 2 Computational Approaches in Biomaterials and Biomedical Engineering Applications, 2024
  • Investigation on Determinants and Choice of Contraceptive Usage among Nigeria Women of Reproductive Age
    Journal of Biostatistics and Epidemiology, 2023
  • Efficient data-mining algorithm for predicting heart disease based on an angiographic test
    Alabi Waheed Banjoko, Kawthar Opeyemi Abdulazeez, and
    Malaysian Journal of Medical Sciences, 2021
    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.
  • Weighted support vector machine algorithm for efficient classification and prediction of binary response data
    A W Banjoko, W B Yahya, M K Garba, K O Abdulazeez
    Journal of Physics Conference Series, 2019
    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.
  • Multiclass Response Feature Selection and Cancer Tumour Classification With Support Vector Machine
    A. W. Banjoko, W. B. Yahya, M. K. Garba
    Journal of Biostatistics and Epidemiology, 2019
    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

RECENT SCHOLAR PUBLICATIONS

  • High resolution class I HLA-A, -B , and - C diversity in Eastern and Southern African populations
    AW Banjoko, T Ng’uni, N Naidoo, V Ramsuran, O Hyrien, ZM Ndhlovu
    Scientific Reports 15 (1), 23667 , 2025
    2025
    Citations: 10
  • The Novel HLA‐E* 01: 152 Allele Identified in African Populations Using Next‐Generation Sequencing
    N Naidoo, TL Ng'uni, U Padia, AW Banjoko, ZM Ndhlovu
    Hla 105 (5), e70247 , 2025
    2025
    Citations: 2
  • A multi-objective optimization algorithm for gene selection and classification in cancer study
    AW Banjoko, WB Yahya, OR Olaniran
    Applied Soft Computing 172, 112911 , 2025
    2025
    Citations: 7
  • Computational Approaches in Biomaterials and Biomedical Engineering Applications
    PD Pathak, R Raut, S Jaramillo-Isaza, P Borkar, RH Jhaveri
    CRC Press , 2024
    2024
    Citations: 1
  • Genetic diagnosis, classification, and risk prediction in cancer using next-generation sequencing in oncology
    KA Dauda, KO Olorede, AW Banjoko, WB Yahya, YO Ayipo
    Computational Approaches in Biomaterials and Biomedical Engineering … , 2024
    2024
    Citations: 3
  • Investigation on Determinants and Choice of Contraceptive Usage among Nigeria Women of Reproductive Age
    AW Banjoko, WB Yahya, MK Garba, RB Afolayan, KA Dauda, ...
    Journal of Biostatistics and Epidemiology , 2023
    2023
    Citations: 1
  • The nexus between foreign aid and foreign direct investment in Nigeria: simultaneous equations approach
    MK Garba, SB Akanni, AW Banjoko, TC Oladele
    Malaysian Journal of Computing (MJoC) 8 (2), 1620-1638 , 2023
    2023
    Citations: 5
  • Effects of foreign direct investment on economic growth: evidence from Nigeria and Rwanda
    MK Garba, AO Sikiru, WB Alabi, RB Afolayan
    Frontiers in Multidisciplinary Research Journal 1 (4), 67-78 , 2022
    2022
  • Modelling determinants of antenatal care services utilization in Nigeria
    AW Banjoko, MK Garba, RB Afolayan, NF Gatta
    Frontiers in Multidisciplinary Research Journal 1 (4), 4-13 , 2022
    2022
    Citations: 1
  • Efficient data-mining algorithm for predicting heart disease based on an angiographic test
    AW Banjoko, KO Abdulazeez
    The Malaysian journal of medical sciences: MJMS 28 (5), 118 , 2021
    2021
    Citations: 5
  • On seemingly unrelated regression and single equation estimators under heteroscedastic error and non-Gaussian responses
    RB Afolayan, AW Banjoko, MK Garba, WB Yahya
    Journal of Engineering and Technology 5 (35) , 2020
    2020
    Citations: 7
  • Data Mining Genome-Based Algorithm for Optimal Gene Selection and Prediction of Colorectal Carcinoma.
    AW Banjoko
    Turkiye Klinikleri Journal of Biostatistics 12 (3) , 2020
    2020
    Citations: 3
  • On the Use of Linear Programming Model Approach in Profit Optimization of a Product Mix Company
    MK Garba, AW Banjoko, WB Yahya, NF Gatta
    Yeast (g) 4 (3.8760), 2.4631 , 2020
    2020
    Citations: 5
  • Econometric Analysis of the Effects of Land Size on Cereals Production in Nigeria
    SB Akanni, MK Garba, AW Banjoko, RB Afolayan
    2020
    Citations: 7
  • Weighted support vector machine algorithm for efficient classification and prediction of binary response data
    AW Banjoko, WB Yahya, MK Garba, KO Abdulazeez
    Journal of Physics: Conference Series 1366 (1), 012101 , 2019
    2019
    Citations: 16
  • Investigating the Effects of Multicollinearity on the Model Parameters of Ordinary Least Squares Estimator
    NF Gatta, BA Waheed
    International Journal of Advances in Scientific Research and Engineering 5 … , 2019
    2019
    Citations: 1
  • Multiclass response feature selection and cancer tumour classification with support vector machine
    AW Banjoko, WB Yahya, MK Garba
    Journal of Biostatistics and Epidemiology , 2019
    2019
    Citations: 7
  • ON THE STRENGTH OF AGREEMENT BETWEEN INITIAL AND FINAL ACADEMIC PERFORMANCES OF STUDENTS IN A NIGERIA UNIVERSITY SYSTEM
    AW Banjoko, WB Yahya, HS Abiodun, RB Afolayan, MK Garba, ...
    ABACUS (Mathematics Science Series) 45 (1) , 2018
    2018
    Citations: 1
  • On the Strength of Agreement between Initial and Final Academic performances in a Nigerian University System
    AW Banjoko, WB Yahya, HS Abiodun, RB Afolayan, MK Garba, ...
    ABACUS, Published by Mathematical Association of Nigeria , 2018
    2018
  • Sequential optimization based feature selection algorithm for efficient cancer classification and prediction
    AW Banjoko, WB Yahya
    4th iSTEAMS International Multidisciplinary Conference 14, 265-74 , 2018
    2018
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Weighted support vector machine algorithm for efficient classification and prediction of binary response data
    AW Banjoko, WB Yahya, MK Garba, KO Abdulazeez
    Journal of Physics: Conference Series 1366 (1), 012101 , 2019
    2019
    Citations: 16
  • Efficient Support Vector Machine Classification of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma mRNA Tissue Samples
    A.W. Banjoko, W. B. Yahya, M. K. Garba, O. R. Olaniran, K. A. Dauda and K. O ...
    Annals. Computer Science Series. 13 (2), 69 - 79 , 2015
    2015
    Citations: 16
  • IMPROVED BAYESIAN FEATURE SELECTION AND CLASSIFICATION METHODS USING BOOTSTRAP PRIOR TECHNIQUES
    OR Olaniran, SF Olaniran, WB Yahya, AW Banjoko, MK Garba, LB Amusa, ...
    Annals. Computer Science Series 14 (2) , 2016
    2016
    Citations: 13
  • SURVIVAL ANALYSIS WITH MULTIVARIATE ADAPTIVE REGRESSION SPLINES USING COX-SNELL RESIDUAL
    KA Dauda, WB Yahya, AW Banjoko
    Annals. Computer Science Series 13 (2), 25 - 41 , 2015
    2015
    Citations: 11
  • High resolution class I HLA-A, -B , and - C diversity in Eastern and Southern African populations
    AW Banjoko, T Ng’uni, N Naidoo, V Ramsuran, O Hyrien, ZM Ndhlovu
    Scientific Reports 15 (1), 23667 , 2025
    2025
    Citations: 10
  • A multi-objective optimization algorithm for gene selection and classification in cancer study
    AW Banjoko, WB Yahya, OR Olaniran
    Applied Soft Computing 172, 112911 , 2025
    2025
    Citations: 7
  • On seemingly unrelated regression and single equation estimators under heteroscedastic error and non-Gaussian responses
    RB Afolayan, AW Banjoko, MK Garba, WB Yahya
    Journal of Engineering and Technology 5 (35) , 2020
    2020
    Citations: 7
  • Econometric Analysis of the Effects of Land Size on Cereals Production in Nigeria
    SB Akanni, MK Garba, AW Banjoko, RB Afolayan
    2020
    Citations: 7
  • Multiclass response feature selection and cancer tumour classification with support vector machine
    AW Banjoko, WB Yahya, MK Garba
    Journal of Biostatistics and Epidemiology , 2019
    2019
    Citations: 7
  • The nexus between foreign aid and foreign direct investment in Nigeria: simultaneous equations approach
    MK Garba, SB Akanni, AW Banjoko, TC Oladele
    Malaysian Journal of Computing (MJoC) 8 (2), 1620-1638 , 2023
    2023
    Citations: 5
  • Efficient data-mining algorithm for predicting heart disease based on an angiographic test
    AW Banjoko, KO Abdulazeez
    The Malaysian journal of medical sciences: MJMS 28 (5), 118 , 2021
    2021
    Citations: 5
  • On the Use of Linear Programming Model Approach in Profit Optimization of a Product Mix Company
    MK Garba, AW Banjoko, WB Yahya, NF Gatta
    Yeast (g) 4 (3.8760), 2.4631 , 2020
    2020
    Citations: 5
  • Genetic diagnosis, classification, and risk prediction in cancer using next-generation sequencing in oncology
    KA Dauda, KO Olorede, AW Banjoko, WB Yahya, YO Ayipo
    Computational Approaches in Biomaterials and Biomedical Engineering … , 2024
    2024
    Citations: 3
  • Data Mining Genome-Based Algorithm for Optimal Gene Selection and Prediction of Colorectal Carcinoma.
    AW Banjoko
    Turkiye Klinikleri Journal of Biostatistics 12 (3) , 2020
    2020
    Citations: 3
  • Sequential optimization based feature selection algorithm for efficient cancer classification and prediction
    AW Banjoko, WB Yahya
    4th iSTEAMS International Multidisciplinary Conference 14, 265-74 , 2018
    2018
    Citations: 3
  • The Novel HLA‐E* 01: 152 Allele Identified in African Populations Using Next‐Generation Sequencing
    N Naidoo, TL Ng'uni, U Padia, AW Banjoko, ZM Ndhlovu
    Hla 105 (5), e70247 , 2025
    2025
    Citations: 2
  • A Test Procedure for Ordered Hypothesis of Population Proportions Against a Control.
    WB Yahya, OR Olaniran, MK Garba, I Oloyede, AW Banjoko, KA Dauda, ...
    Turkiye Klinikleri Journal of Biostatistics 8 (1) , 2016
    2016
    Citations: 2
  • Computational Approaches in Biomaterials and Biomedical Engineering Applications
    PD Pathak, R Raut, S Jaramillo-Isaza, P Borkar, RH Jhaveri
    CRC Press , 2024
    2024
    Citations: 1
  • Investigation on Determinants and Choice of Contraceptive Usage among Nigeria Women of Reproductive Age
    AW Banjoko, WB Yahya, MK Garba, RB Afolayan, KA Dauda, ...
    Journal of Biostatistics and Epidemiology , 2023
    2023
    Citations: 1
  • Modelling determinants of antenatal care services utilization in Nigeria
    AW Banjoko, MK Garba, RB Afolayan, NF Gatta
    Frontiers in Multidisciplinary Research Journal 1 (4), 4-13 , 2022
    2022
    Citations: 1