An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction
Deep Forest employs forest structures and leverages deep architecture to learn feature vector information adaptively. However, deep forest-based models have limitations such as manual hyperparameter optimization and time and memory usage inefficiencies. Bayesian optimization is a widely used model-b...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| author | Jerry Emmanuel Itunuoluwa Isewon Jelili Oyelade |
| author_facet | Jerry Emmanuel Itunuoluwa Isewon Jelili Oyelade |
| author_sort | Jerry Emmanuel |
| collection | DOAJ |
| description | Deep Forest employs forest structures and leverages deep architecture to learn feature vector information adaptively. However, deep forest-based models have limitations such as manual hyperparameter optimization and time and memory usage inefficiencies. Bayesian optimization is a widely used model-based hyperparameter optimization method. Evolutionary algorithms such as Differential Evolution (DE) have recently been introduced to improve Bayesian optimization’s acquisition function. Despite its effectiveness, DE has a significant drawback as it relies on randomly selecting indices from the population of target vectors to construct donor vectors in search of optimal solutions. This randomness is ineffective, as suboptimal or redundant indices may be selected. Therefore, in this research we developed a modified differential evolution (DE) acquisition function for improved host-pathogen protein-protein interaction prediction. The modified DE introduces a weighted and adaptive donor vector technique that selects the best-fitted donor vectors as opposed to the random approach. This modified optimization approach was implemented in a deep forest model for automatic hyperparameter optimization. The performance of the optimized deep forest model was evaluated on human-Plasmodium falciparum protein sequence datasets using 10-fold cross-validation. The results were compared with standard optimization methods such as traditional Bayesian optimization, genetic algorithms, evolutionary strategies, and other machine learning models. The optimized model achieved an accuracy of 89.3 %, outperforming other models across all metrics, including a sensitivity of 85.4 % and a precision of 91.6 %. Additionally, the optimized model predicted seven novel host-pathogen interactions. Finally, the model was implemented as a web application which is accessible at http://dfh3pi.covenantuniversity.edu.ng. |
| format | Article |
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| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-93c99873907c43998c49c899adf107ed2025-02-08T05:00:07ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127595611An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction predictionJerry Emmanuel0Itunuoluwa Isewon1Jelili Oyelade2Department of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), Nigeria; Covenant University Bioinformatics Research (CUBRe), NigeriaDepartment of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), Nigeria; Covenant University Bioinformatics Research (CUBRe), NigeriaDepartment of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), Nigeria; Covenant University Bioinformatics Research (CUBRe), Nigeria; Corresponding author at: Department of Computer and Information Sciences, Covenant University, Ota, Nigeria.Deep Forest employs forest structures and leverages deep architecture to learn feature vector information adaptively. However, deep forest-based models have limitations such as manual hyperparameter optimization and time and memory usage inefficiencies. Bayesian optimization is a widely used model-based hyperparameter optimization method. Evolutionary algorithms such as Differential Evolution (DE) have recently been introduced to improve Bayesian optimization’s acquisition function. Despite its effectiveness, DE has a significant drawback as it relies on randomly selecting indices from the population of target vectors to construct donor vectors in search of optimal solutions. This randomness is ineffective, as suboptimal or redundant indices may be selected. Therefore, in this research we developed a modified differential evolution (DE) acquisition function for improved host-pathogen protein-protein interaction prediction. The modified DE introduces a weighted and adaptive donor vector technique that selects the best-fitted donor vectors as opposed to the random approach. This modified optimization approach was implemented in a deep forest model for automatic hyperparameter optimization. The performance of the optimized deep forest model was evaluated on human-Plasmodium falciparum protein sequence datasets using 10-fold cross-validation. The results were compared with standard optimization methods such as traditional Bayesian optimization, genetic algorithms, evolutionary strategies, and other machine learning models. The optimized model achieved an accuracy of 89.3 %, outperforming other models across all metrics, including a sensitivity of 85.4 % and a precision of 91.6 %. Additionally, the optimized model predicted seven novel host-pathogen interactions. Finally, the model was implemented as a web application which is accessible at http://dfh3pi.covenantuniversity.edu.ng.http://www.sciencedirect.com/science/article/pii/S2001037025000212Deep forestDifferential evolutionOptimizationHyperparameterProtein-protein interactionPlasmodium falciparum |
| spellingShingle | Jerry Emmanuel Itunuoluwa Isewon Jelili Oyelade An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction Computational and Structural Biotechnology Journal Deep forest Differential evolution Optimization Hyperparameter Protein-protein interaction Plasmodium falciparum |
| title | An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction |
| title_full | An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction |
| title_fullStr | An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction |
| title_full_unstemmed | An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction |
| title_short | An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction |
| title_sort | optimized deep forest algorithm using a modified differential evolution optimization algorithm a case of host pathogen protein protein interaction prediction |
| topic | Deep forest Differential evolution Optimization Hyperparameter Protein-protein interaction Plasmodium falciparum |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025000212 |
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