Olutomilayo Olayemi PETINRIN

Olutomilayo Olayemi PETINRIN

Data Science || Health-related research

Publications

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer

Published in Biochemical Genetics, 2023

The study aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients.

Recommended citation: Zhe, L., Petinrin, O.O., Toseef, M., Chen„ N., & Wong, K. C. (2023). "Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer". Biochemical Genetics, 1-28. https://doi.org/10.1007/s10528-023-10516-4

Deep Transfer Learning for Clinical Decision-Making Based on High-Throughput Data: Comprehensive Survey with Benchmark Results

Published in Briefings in Bioinformatics, 2023

This is a review paper about applying deep transfer learning to high-throughput data for improving clinical decision-making.

Recommended citation: Toseef, M., Petinrin, O.O., Wang, F., Rahaman, S., Liu, Z., Li, X., & Wong, K. C. (2023). "Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results". Briefings in Bioinformatics, bbad254. https://doi.org/10.1093/bib/bbad254

Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification

Published in Processes, 2023

The study used SVMSMOTE for the oversampling of the six examined datasets.

Recommended citation: Petinrin, O. O., Saeed, F., Salim, N., Toseef, M., Liu, Z., & Muyide, I. O. (2023). "Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification". Processes, 11(7), 1940. https://doi.org/10.3390/pr11071940

Machine Learning in Metastatic Cancer Research: Potentials, Possibilities, and Prospects

Published in Computational and Structural Biotechnology Journal, 2023

This is a review paper about the application of machine learning in several aspects of metastatic cancer research.

Recommended citation: Petinrin, O.O., Saeed, F., Toseef, M., Liu, Z., Basurra, S., Muyide, I.O., Li, X., Lin, Q. and Wong, K.C. (2023). "Machine Learning in Metastatic Cancer Research: Potentials, Possibilities, and Prospects". Computational and Structural Biotechnology Journal Vol. 21, 2454-2470. https://doi.org/10.1016/j.csbj.2023.03.046

Reactions’ Descriptors Selection and Yield Estimation using Metaheuristic Algorithms and Voting Ensemble

Published in Computers, Materials and Continua, 2022

This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation.

Recommended citation: Petinrin, O. O., Saeed, F., Li, X., Ghabban, F., & Wong, K. C. (2022). "Reactions’ descriptors selection and yield estimation using metaheuristic algorithms and voting ensemble". Computers, Materials and Continua, 70(3), 4745-4762. https://doi.org/10.32604/cmc.2022.020523

The Comprehensive and Systematic Identification of BLCA-Specific SF-Regulated, Survival-Related AS Events

Published in Gene, Elsevier, 2022

This study provides an AS-SF regulation network consisting of five SFs and 46 AS events.

Recommended citation: Liu, Z., Liu, X., Liu, F., Zhao, H., Zhang, Y., Wang, Y., Ma, Y., Wang, F., Zhang, W., Petinrin, O.O., Yao, Z., Liang, J., He, Q., Feng, D., Wang, L., & Wong, K. C. (2022). "The comprehensive and systematic identification of BLCA-specific SF-regulated, survival-related AS events". Gene, 835, 146657. https://doi.org/10.1016/j.gene.2022.146657

Particle Swarm Optimized Gaussian Process Classifier for Treatment Discontinuation Prediction in Multicohort Metastatic Castration-Resistant Prostate Cancer Patients

Published in IEEE Journal of Biomedical and Health Informatics, 2021

In this study, we propose the optimization of a machine learning method using metaheuristic algorithm for the determination of treatment discontinuation in mCRPC patients.

Recommended citation: Petinrin, O. O., Li, X., & Wong, K. C. (2021). "Particle Swarm Optimized Gaussian Process Classifier for Treatment Discontinuation Prediction in Multicohort Metastatic Castration-Resistant Prostate Cancer Patients". IEEE Journal of Biomedical and Health Informatics, 26(3), 1309-1317. https:www.doi.org/10.1109/JBHI.2021.3103989

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Published in Life, MDPI, 2021

In this survey, we discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

Recommended citation: Liu, L., Chen, X., Petinrin, O. O., Zhang, W., Rahaman, S., Tang, Z. R., & Wong, K. C. (2021). "Machine learning protocols in early cancer detection based on liquid biopsy: a survey". Life, 11(7), 638. https://doi.org/10.3390/life11070638

Optimal Distributed Generation Location and Sizing for Loss Minimization and Voltage Profile Optimization using Ant Colony Algorithm

Published in SN Applied Sciences, 2021

This paper proposes an approach for optimum sizing and siting of DGs sizing in a power distribution system using Ant Colony Optimization (ACO) algorithm.

Recommended citation: Ogunsina, A. A., Petinrin, M. O., Petinrin, O. O., Offornedo, E. N., Petinrin, J. O., & Asaolu, G. O. (2021). "Optimal distributed generation location and sizing for loss minimization and voltage profile optimization using ant colony algorithm". SN Applied Sciences, 3(2), 1-10. https://doi.org/10.1007/s42452-021-04226-y

High-Performance Computing for Computational Modelling in Built Environment-Related Studies – a Scientometric Review

Published in Journal of Engineering, Design and Technology, 2020

This paper presents the result of a scientometric analysis conducted using studies on high-performance computing in computational modelling.

Recommended citation: Aghimien, E. I., Aghimien, L. M., Petinrin, O. O., & Aghimien, D. O. (2020). "High-performance computing for computational modelling in built environment-related studies–a scientometric review". Journal of Engineering, Design and Technology, Vol. 19 No. 5, pp. 1138-1157.. https://doi.org/10.1108/JEDT-07-2020-0294

Stacked Ensemble for Bioactive Molecule Prediction

Published in IEEE Access, 2019

This study utilizes the stacked ensemble which uses the prediction of multiple base classifiers as features, used to train a meta classifier which makes the final prediction on datasets from MDL Drug Data Report (MDDR) database.

Recommended citation: Petinrin, O. O., & Saeed, F. (2019). "Stacked ensemble for bioactive molecule prediction". IEEE Access, 7, 153952-153957. https://doi.org/10.1109/ACCESS.2019.2945422

Filter-Wrapper Combination and Embedded Feature Selection for Gene Expression Data

Published in International Journal of Advances in Soft Computing and its Applications, 2018

This study utilised Filter-Wrapper combination and embedded (LASSO) feature selection methods on both high and low dimensional datasets before classification was performed.

Recommended citation: Hameed, S. S., Petinrin, O. O., Hashi, A. O., & Saeed, F. (2018). "Filter-wrapper combination and embedded feature selection for gene expression data". Int. J. Advance Soft Compu. Appl, 10(1), 90-105.

Bioactive Molecule Prediction using Majority Voting-Based Ensemble Method

Published in Journal of Intelligent & Fuzzy Systems, 2018

In this study, majority voting ensemble method was used to determine biologically active molecular compounds which are capable of drug-target interaction.

Recommended citation: Petinrin, O. O., & Saeed, F. (2018). "Bioactive molecule prediction using majority voting-based ensemble method". Journal of Intelligent & Fuzzy Systems, 35(1), 383-392. https://doi.org/10.3233/JIFS-169596

Voting-Based Ensemble Method for Prediction of Bioactive Molecules

Published in International Conference on Knowledge Engineering and Applications (ICKEA), IEEE, 2017

In this study, we utilize voting ensembe in the prediction of bioactive molecular compounds for drug discovery.

Recommended citation: Petinrin, O. O., Saeed, F., & Al-Hadhrami, T. (2017, October). "Voting-based ensemble method for prediction of bioactive molecules". In 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA) (pp. 118-122). IEEE. https://doi.org/10.1109/ICKEA.2017.8169913

A Digital Technology Framework for Promoting Nutritional/Health Benefits of West African Diet

Published in International Conference of Science, Engineering and Social Sciences (ICSESS), 2017

In this study, we report factors that are responsible for the gradual adoption of the high-calorie, high-fat, high-sodium and highly processed unhealthy foods

Recommended citation: Muniru, I. O., Shifulah, N., Petinrin, O. O., & Utama, N. P. (2017). "A Digital Technology Framework for Promoting Nutritional/Health Benefits of West African Diet". International Conference of Science, Engineering and Social Sciences (ICSESS) (29), 201-203.

Expert System for Diagnosis of Malaria and Typhoid Fever

Published in International Conference of Science, Engineering & Environmental Technology (ICONSEET), 2017

This paper shows how expert systems will improve the immediate, accurate and reliable diagnosis of malaria and typhoid fever

Recommended citation: Olatunbosun, K., & Petinrin, O. O. (2017). "Expert System for Diagnosis of Malaria and Typhoid Fever". Technology (ICONSEET), 2(44), 341-346.

Application of Machine Learning in Prediction of Bioactivity of Molecular Compounds: A Review

Published in International Conference of Science, Engineering & Environmental Technology (ICONSEET), 2017

A review on the use of machine learning for prediction of bioactive molecular compounds.

Recommended citation: Petinrin, O. O. & Olatunbosun, K. (2017) "Application of machine learning in prediction of bioactivity of molecular compounds: A review." Technology (ICONSEET), 2(2), 9-15.