PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation. Issue 1 (26th September 2014)
- Record Type:
- Journal Article
- Title:
- PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation. Issue 1 (26th September 2014)
- Main Title:
- PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation
- Authors:
- Liu, Bin
Xu, Jinghao
Fan, Shixi
Xu, Ruifeng
Zhou, Jiyun
Wang, Xiaolong - Abstract:
- <abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Identification of DNA‐binding proteins is an important problem in biomedical research as DNA‐binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the‐state‐of‐the‐art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of proteins. In this study, we proposed a feature vector composed of three kinds of sequence‐based features, including overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider the sequence composition of proteins, but also incorporate the sequence‐order information of amino acids in proteins. The feature vectors were fed into Support Vector Machine (SVM) for DNA‐binding protein identification. The proposed method is called PseDNA‐Pro. Experiments on stringent benchmark datasets and independent test datasets by using the Jackknife test showed that PseDNA‐Pro can achieve an accuracy of higher than 80 %, outperforming several state‐of‐the‐art methods, including DNAbinder, DNA‐Prot, and iDNA‐Prot. These results indicate that the combination of various features for DNA‐binding protein prediction is a suitable approach, and the sequence‐order information among residues in proteins is relative for discrimination. For practical applications, a web‐server of<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Identification of DNA‐binding proteins is an important problem in biomedical research as DNA‐binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the‐state‐of‐the‐art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of proteins. In this study, we proposed a feature vector composed of three kinds of sequence‐based features, including overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider the sequence composition of proteins, but also incorporate the sequence‐order information of amino acids in proteins. The feature vectors were fed into Support Vector Machine (SVM) for DNA‐binding protein identification. The proposed method is called PseDNA‐Pro. Experiments on stringent benchmark datasets and independent test datasets by using the Jackknife test showed that PseDNA‐Pro can achieve an accuracy of higher than 80 %, outperforming several state‐of‐the‐art methods, including DNAbinder, DNA‐Prot, and iDNA‐Prot. These results indicate that the combination of various features for DNA‐binding protein prediction is a suitable approach, and the sequence‐order information among residues in proteins is relative for discrimination. For practical applications, a web‐server of PseDNA‐Pro was established, which is available from http://bioinformatics.hitsz.edu.cn/PseDNA‐Pro/.</p> </abstract> … (more)
- Is Part Of:
- Molecular informatics. Volume 34:Issue 1(2015:Jan.)
- Journal:
- Molecular informatics
- Issue:
- Volume 34:Issue 1(2015:Jan.)
- Issue Display:
- Volume 34, Issue 1 (2015)
- Year:
- 2015
- Volume:
- 34
- Issue:
- 1
- Issue Sort Value:
- 2015-0034-0001-0000
- Page Start:
- 8
- Page End:
- 17
- Publication Date:
- 2014-09-26
- Subjects:
- Cheminformatics -- Periodicals
QSAR (Biochemistry) -- Periodicals
Structure-activity relationships (Biochemistry) -- Periodicals
Drugs -- Structure-activity relationships -- Periodicals
615.19 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1868-1751 ↗
http://www3.interscience.wiley.com/journal/123236613/home ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/minf.201400025 ↗
- Languages:
- English
- ISSNs:
- 1868-1743
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5900.817750
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4342.xml