Name
Title | ||
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EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation |
Abstract | ||
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Background: Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. Results: In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. Conclusions: We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community. © 2017 The Author(s). |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12859-017-1792-8 | BMC Bioinformatics |
Keywords | Field | DocType |
DNA-protein interaction,DNA-binding residue,PSSM,Ensemble learning,SVM,Random forest,Relation transformation | Residue (complex analysis),Computer science,Data imbalance,Artificial intelligence,Random forest,Classifier (linguistics),Ensemble learning,ENCODE,Pattern recognition,Support vector machine,Bioinformatics,Machine learning,Encoding (memory) | Journal |
Volume | Issue | ISSN |
18 | 1 | 1471-2105 |
Citations | PageRank | References |
4 | 0.38 | 29 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou Jiyun | 1 | 16 | 2.97 |
| Qin Lu | 2 | 689 | 66.45 |
| Xu Ruifeng | 3 | 432 | 53.04 |
| Yulan He | 4 | 1934 | 123.88 |
| Wang Hongpeng | 5 | 75 | 12.46 |