ArticleComputational and mathematical methods in medicine2013
Identification of DNA-binding proteins using support vector machine with sequence information.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Systematic discovery of DNA-binding tandem repeat proteins.Nucleic acids research · 2024Article
- Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.Nucleic acids research · 2018Article
- BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins.Advances in bioinformatics · 2016Article
- DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.PloS one · 2016Article
- DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.Nucleic acids research · 2015Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
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Authors and funding
3 authors.
Funding
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Abstract
DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.
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