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Title: | EL_PSSM-RT : DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation | Authors: | Zhou, JY Lu, Q Xu, RF He, YL Wang, HP |
Issue Date: | 2017 | Source: | BMC bioinformatics, 2017, v. 18, 379, p. 1-16 | Abstract: | 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. | Keywords: | DNA-protein interaction DNA-binding residue PSSM Ensemble learning SVM Random forest Relation transformation |
Publisher: | BioMed Central | Journal: | BMC bioinformatics | ISSN: | 1471-2105 | EISSN: | 1471-2105 | DOI: | 10.1186/s12859-017-1792-8 | Rights: | © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. The following publication Zhou, J. Y., Lu, Q., Xu, R. F., He, Y. L., & Wang, H. P. (2017). EL_PSSM-RT : DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation. BMC Bioinformatics, 18, 379, 1-16 is available at https://dx.doi.org/10.1186/s12859-017-1792-8 |
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Zhou_El_Pssm-Rt_Dna-Binding.pdf | 2.06 MB | Adobe PDF | View/Open |
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