Shape string : A new feature for prediction of DNA-binding residues
(2013) In Biochimie 95(2). p.354-358- Abstract
Protein-DNA interactions are involved in many biological processes essential for gene expression and regulation. To understand the molecular mechanisms of protein-DNA recognition, it is crucial to analyze and identify DNA-binding residues of protein-DNA complexes. Here, we proposed a novel descriptor shape string and another two related features shape string PSSM and shape string pair composition to characterize DNA-binding residues. We employed the new features and the position-specific scoring matrix (PSSM) for modeling and prediction. The results of a benchmark dataset showed that our approach significantly improved the accuracy of the predictor. The overall accuracy of our approach reached 85.86% with 85.02% sensitivity and 86.02%... (More)
Protein-DNA interactions are involved in many biological processes essential for gene expression and regulation. To understand the molecular mechanisms of protein-DNA recognition, it is crucial to analyze and identify DNA-binding residues of protein-DNA complexes. Here, we proposed a novel descriptor shape string and another two related features shape string PSSM and shape string pair composition to characterize DNA-binding residues. We employed the new features and the position-specific scoring matrix (PSSM) for modeling and prediction. The results of a benchmark dataset showed that our approach significantly improved the accuracy of the predictor. The overall accuracy of our approach reached 85.86% with 85.02% sensitivity and 86.02% specificity. The results also demonstrated that shape string is a powerful descriptor for the prediction of DNA-binding residues. The additional two related features enhanced the predictive value.
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- author
- Wang, Duo Duo ; Li, Tong Hua ; Sun, Jiang Ming LU ; Li, Da Peng ; Xiong, Wen Wei ; Wang, Wen Yan and Tang, Sheng Nan
- publishing date
- 2013-02
- type
- Contribution to journal
- publication status
- published
- keywords
- Machine learning, Shape string, Shape string pair composition, Shape string PSSM, Support vector machine (SVM), Protein-DNA interaction
- in
- Biochimie
- volume
- 95
- issue
- 2
- pages
- 354 - 358
- publisher
- Elsevier
- external identifiers
-
- pmid:23116714
- scopus:84872821520
- ISSN
- 0300-9084
- DOI
- 10.1016/j.biochi.2012.10.006
- language
- English
- LU publication?
- no
- id
- 30ba6b96-a9f7-4479-8176-d3998af9af8d
- date added to LUP
- 2023-04-26 12:34:37
- date last changed
- 2024-06-29 03:23:20
@article{30ba6b96-a9f7-4479-8176-d3998af9af8d, abstract = {{<p>Protein-DNA interactions are involved in many biological processes essential for gene expression and regulation. To understand the molecular mechanisms of protein-DNA recognition, it is crucial to analyze and identify DNA-binding residues of protein-DNA complexes. Here, we proposed a novel descriptor shape string and another two related features shape string PSSM and shape string pair composition to characterize DNA-binding residues. We employed the new features and the position-specific scoring matrix (PSSM) for modeling and prediction. The results of a benchmark dataset showed that our approach significantly improved the accuracy of the predictor. The overall accuracy of our approach reached 85.86% with 85.02% sensitivity and 86.02% specificity. The results also demonstrated that shape string is a powerful descriptor for the prediction of DNA-binding residues. The additional two related features enhanced the predictive value.</p>}}, author = {{Wang, Duo Duo and Li, Tong Hua and Sun, Jiang Ming and Li, Da Peng and Xiong, Wen Wei and Wang, Wen Yan and Tang, Sheng Nan}}, issn = {{0300-9084}}, keywords = {{Machine learning; Shape string; Shape string pair composition; Shape string PSSM; Support vector machine (SVM); Protein-DNA interaction}}, language = {{eng}}, number = {{2}}, pages = {{354--358}}, publisher = {{Elsevier}}, series = {{Biochimie}}, title = {{Shape string : A new feature for prediction of DNA-binding residues}}, url = {{http://dx.doi.org/10.1016/j.biochi.2012.10.006}}, doi = {{10.1016/j.biochi.2012.10.006}}, volume = {{95}}, year = {{2013}}, }