ArticleBMC bioinformatics2024
StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
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- Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.Molecular diversity · 2025Article
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- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
- StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models.Bioinformatics advances · 2025Article
- NFEmbed: modeling nitrogenase activity via classification and regression with pretrained protein embeddings.Bioinformatics advances · 2025Article
- Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.PloS one · 2025Article
- An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection.PloS one · 2025Article
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- Classification of pulmonary diseases from chest radiographs using deep transfer learning.PloS one · 2025Article
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- Smart waste classification in IoT-enabled smart cities using VGG16 and Cat Swarm Optimized random forest.PloS one · 2025Article
- Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.Scientific reports · 2024Article
- AI based predictive acceptability model for effective vaccine delivery in healthcare systems.Scientific reports · 2024Article
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5 authors.
Funding
Abstract
backgroundAntioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins.
methodsIn this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model.
resultsOur developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98.
conclusionOur proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.
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