SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
Open accessAbstract
Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.
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An Efficient Classifier for Alzheimer’s Disease Genes Identification
Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107 · see citing works
Self-citationXuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00459 · Cited by 100 · see citing works
- Authors
- Xu, L., Liang, G., Shi, S., & Liao, C.
- Affiliations
- Shenzhen Polytechnic University (CN)
- Shenzhen University (CN)
- Corresponding author
- Liang, Guangmin
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2018-06-15
- Volume
- 19
- Issue
- 6
- Pages
- 1773
- ISSN
- 1422-0067 (ISSN-L)
- PMC
- PMC6032279
- OpenAlex
- W2808487499
- Cited by
- 95
- References
- 66
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 1 · 2023: 7 · 2022: 5 · 2021: 26 · 2020: 34 · 2019: 14 · 2018: 1
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (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 Nature Science Foundation of China (61575128, 61771331)
- Science and Technology Innovation Commission of Shenzhen (JCYJ20160523113602609)
- 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 › vaccines and immunoinformatics approaches
- Keywords
- antioxidant proteins, protein function prediction, sequence-based prediction, support vector machine, feature selection, protein classification
- MeSH terms
- Protein Domains, Animals, Humans, Software, Oxidative Stress, Sequence Analysis, Protein, Support Vector Machine
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