Prediction of splice site using AdaBoost with a new sequence encoding approach

Elham Pashaei; Alper Yilmaz; Mustafa Ozen; Nizamettin Aydin

doi:10.1109/SMC.2016.7844835

Prediction of splice site using AdaBoost with a new sequence encoding approach

Elham Pashaei, Alper Yilmaz, Mustafa Ozen, Nizamettin Aydin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

The Biological sequence data are increasing rapidly, so there is a vital need of effective method for gene detection. Predicting of splice site is an important part of gene finding. Therefore, attempts to improve the prediction accuracy of the computational methods for splice sites detection continue. In this paper we propose a hybrid algorithm for splice sites prediction by combining AdaBoost classifier with a novel nucleotide encoding method, namely FDDM. Our encoding method provides frequency difference between the true sites and false sites (FD) along with distance measure (DM). The proposed method produces an improvement in comparison with the result of current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MSC+Pos(+APR)-SVM, when applied to the HS3D dataset with repeated 10-fold cross validation. In addition, for demonstrating the stability of the method, we also applied it to NN269 dataset. The obtained results indicate that the new method is practicable and efficient.

Original language	English
Title of host publication	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3853-3858
Number of pages	6
ISBN (Electronic)	9781509018970
DOIs	https://doi.org/10.1109/SMC.2016.7844835
Publication status	Published - 6 Feb 2017
Externally published	Yes
Event	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: 9 Oct 2016 → 12 Oct 2016

Publication series

Name	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Conference

Conference	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Country/Territory	Hungary
City	Budapest
Period	9/10/16 → 12/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

AdaBoost classifier
Nucleotide encoding method
Splice site prediction

Access to Document

10.1109/SMC.2016.7844835

Cite this

Pashaei, E., Yilmaz, A., Ozen, M., & Aydin, N. (2017). Prediction of splice site using AdaBoost with a new sequence encoding approach. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 3853-3858). Article 7844835 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844835

Pashaei, Elham ; Yilmaz, Alper ; Ozen, Mustafa et al. / Prediction of splice site using AdaBoost with a new sequence encoding approach. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3853-3858 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings).

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title = "Prediction of splice site using AdaBoost with a new sequence encoding approach",

abstract = "The Biological sequence data are increasing rapidly, so there is a vital need of effective method for gene detection. Predicting of splice site is an important part of gene finding. Therefore, attempts to improve the prediction accuracy of the computational methods for splice sites detection continue. In this paper we propose a hybrid algorithm for splice sites prediction by combining AdaBoost classifier with a novel nucleotide encoding method, namely FDDM. Our encoding method provides frequency difference between the true sites and false sites (FD) along with distance measure (DM). The proposed method produces an improvement in comparison with the result of current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MSC+Pos(+APR)-SVM, when applied to the HS3D dataset with repeated 10-fold cross validation. In addition, for demonstrating the stability of the method, we also applied it to NN269 dataset. The obtained results indicate that the new method is practicable and efficient.",

keywords = "AdaBoost classifier, Nucleotide encoding method, Splice site prediction",

author = "Elham Pashaei and Alper Yilmaz and Mustafa Ozen and Nizamettin Aydin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 ; Conference date: 09-10-2016 Through 12-10-2016",

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Pashaei, E, Yilmaz, A, Ozen, M & Aydin, N 2017, Prediction of splice site using AdaBoost with a new sequence encoding approach. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings., 7844835, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 3853-3858, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 9/10/16. https://doi.org/10.1109/SMC.2016.7844835

Prediction of splice site using AdaBoost with a new sequence encoding approach. / Pashaei, Elham; Yilmaz, Alper; Ozen, Mustafa et al.
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3853-3858 7844835 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Pashaei, Elham

AU - Yilmaz, Alper

AU - Ozen, Mustafa

AU - Aydin, Nizamettin

PY - 2017/2/6

Y1 - 2017/2/6

N2 - The Biological sequence data are increasing rapidly, so there is a vital need of effective method for gene detection. Predicting of splice site is an important part of gene finding. Therefore, attempts to improve the prediction accuracy of the computational methods for splice sites detection continue. In this paper we propose a hybrid algorithm for splice sites prediction by combining AdaBoost classifier with a novel nucleotide encoding method, namely FDDM. Our encoding method provides frequency difference between the true sites and false sites (FD) along with distance measure (DM). The proposed method produces an improvement in comparison with the result of current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MSC+Pos(+APR)-SVM, when applied to the HS3D dataset with repeated 10-fold cross validation. In addition, for demonstrating the stability of the method, we also applied it to NN269 dataset. The obtained results indicate that the new method is practicable and efficient.

AB - The Biological sequence data are increasing rapidly, so there is a vital need of effective method for gene detection. Predicting of splice site is an important part of gene finding. Therefore, attempts to improve the prediction accuracy of the computational methods for splice sites detection continue. In this paper we propose a hybrid algorithm for splice sites prediction by combining AdaBoost classifier with a novel nucleotide encoding method, namely FDDM. Our encoding method provides frequency difference between the true sites and false sites (FD) along with distance measure (DM). The proposed method produces an improvement in comparison with the result of current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MSC+Pos(+APR)-SVM, when applied to the HS3D dataset with repeated 10-fold cross validation. In addition, for demonstrating the stability of the method, we also applied it to NN269 dataset. The obtained results indicate that the new method is practicable and efficient.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Pashaei E, Yilmaz A, Ozen M, Aydin N. Prediction of splice site using AdaBoost with a new sequence encoding approach. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3853-3858. 7844835. (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings). doi: 10.1109/SMC.2016.7844835

Prediction of splice site using AdaBoost with a new sequence encoding approach

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Keywords

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