ArticleFrontiers in bioengineering and biotechnology2019
AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine.
Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.NAR genomics and bioinformatics · 2025Article
- Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features.Genomics & informatics · 2024Article
- Classification of bioactive peptides: A systematic benchmark of models and encodings.Computational and structural biotechnology journal · 2024Article
- StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.BMC bioinformatics · 2024Article
- Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.BMC bioinformatics · 2024Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties.BMC bioinformatics · 2022Article
- The Interplay of Oxidative Stress and ROS Scavenging: Antioxidants as a Therapeutic Potential in Sepsis.Vaccines · 2022Review
- A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia.Molecular therapy. Nucleic acids · 2022Article
- High Throughput Identification of the Potential Antioxidant Peptides inMolecules (Basel, Switzerland) · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- Identification of intelligence-related proteins through a robust two-layer predictor.Communicative & integrative biology · 2022Article
- Article
- Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review.Molecular diversity · 2021Review
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
- Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.Computational and mathematical methods in medicine · 2021Article
- ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy andComputational and mathematical methods in medicine · 2021Article
- AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.Frontiers in pharmacology · 2021Review
- An Interpretable Prediction Model for Identifying NMolecular therapy. Nucleic acids · 2020Article
- Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors.BMC bioinformatics · 2020Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.
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