Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information
| Autor: | Xin Ma, Xiaoyun Xue, Jiansheng Wu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Support Vector Machine
Correlation coefficient Article Subject Property (programming) Computer science computer.software_genre lcsh:Computer applications to medicine. Medical informatics General Biochemistry Genetics and Molecular Biology Artificial Intelligence Sensitivity (control systems) Amino Acid Sequence Databases Protein Sequence Binding Sites General Immunology and Microbiology Applied Mathematics General Medicine Support vector machine DNA-Binding Proteins Identification (information) Feature (computer vision) Modeling and Simulation lcsh:R858-859.7 Data mining Performance improvement computer Algorithms Research Article |
| Zdroj: | Computational and Mathematical Methods in Medicine Computational and Mathematical Methods in Medicine, Vol 2013 (2013) |
| ISSN: | 1748-670X |
| DOI: | 10.1155/2013/524502 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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