A combined SVM and Markov model approach for splice site identification

Elham Pashaei, Alper Yilmaz, Nizamettin Aydin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Due to an exponential increase in biological sequence data, gene detection has become one of the challenging tasks in computational biology. Splice site prediction is an essential part of the gene detection. Thus, it has great significance to develop efficient methods for accurately identifying splice sites. This paper introduces a novel algorithm to predict the splice sites based on support vector machine (SVM) and a new type of Markov chain model, namely DMM2. The proposed method shows great improvement over most of the current state of art methods, including MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, MM1-RF, MM2F-SVM, MCM-SVM, DM-SVM and DM2-AdaBoost. The repeated 10-fold cross validation was used to assess the performance of the method on the HS3D dataset. In addition, we applied it to NN269 dataset to examine the stability of the proposed method. The experimental results indicate that the new approach is feasible and efficient.

Original languageEnglish
Title of host publication2016 6th International Conference on Computer and Knowledge Engineering, ICCKE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages200-204
Number of pages5
ISBN (Electronic)9781509035861
DOIs
Publication statusPublished - 29 Dec 2016
Externally publishedYes
Event6th International Conference on Computer and Knowledge Engineering, ICCKE 2016 - Mashhad, Iran, Islamic Republic of
Duration: 20 Oct 2016 → …

Publication series

Name2016 6th International Conference on Computer and Knowledge Engineering, ICCKE 2016

Conference

Conference6th International Conference on Computer and Knowledge Engineering, ICCKE 2016
Country/TerritoryIran, Islamic Republic of
CityMashhad
Period20/10/16 → …

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • nucleotide encoding method
  • second order Markov model
  • splice site prediction
  • SVM classifier

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