Identification of Antioxidants from Sequence Information Using Naïve Bayes
Open accessAbstract
Antioxidant proteins are substances that protect cells from the damage caused by free radicals. Accurate identification of new antioxidant proteins is important in understanding their roles in delaying aging. Therefore, it is highly desirable to develop computational methods to identify antioxidant proteins. In this study, a Naïve Bayes-based method was proposed to predict antioxidant proteins using amino acid compositions and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife test, the proposed method achieved an accuracy of 66.88% for the discrimination between antioxidant and nonantioxidant proteins, which is superior to that of other state-of-the-art classifiers. These results suggest that the proposed method could be an effective and promising high-throughput method for antioxidant protein identification.
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- Authors
- Feng, P.-M., Lin, H., & Chen, W.
- Affiliations
- School of Public Health, Hebei United University, Tangshan 063000, China
- University of Electronic Science and Technology of China (CN)
- North China University of Science and Technology (CN)
- Corresponding author
- Lin, Hao
- Chen, Wei
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2013
- Volume
- 2013
- Pages
- 1-5
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC3766563
- OpenAlex
- W2157642860
- Cited by
- 109
- References
- 32
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 13 · 2020: 20 · 2019: 24 · 2018: 27 · 2017: 2 · 2016: 2 · 2015: 4 · 2014: 6
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61100092, 61202256, ZYGX2012J113)
- Fundamental Research Funds for the Central Universities (61100092, 61202256, ZYGX2012J113)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- antioxidant proteins, protein classification, amino acid composition, dipeptide composition, feature selection, Naive Bayes classifier, free radicals, aging
- MeSH terms
- Algorithms, Amino Acids, Antioxidants, Bayes Theorem, Dipeptides, Humans, Proteins, Databases, Protein, High-Throughput Screening Assays
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