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Identification of antioxidants from sequence information using naive Bayes.

ABSTRACT: 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.

SUBMITTER: Feng PM

PROVIDER: S-EPMC3766563 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Identification of antioxidants from sequence information using naïve Bayes.

Feng Peng-Mian PM Lin Hao H Chen Wei W

Computational and mathematical methods in medicine 20130824

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 n ...[more]

PMID: 24062796

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Dataset Information

Identification of antioxidants from sequence information using naive Bayes.

Publications

Identification of antioxidants from sequence information using naïve Bayes.

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