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Improving the Accuracy of Classifiers for the Prediction of Translation Initiation Sites in Genomic Sequences

  • George Tzanis
  • Christos Berberidis
  • Anastasia Alexandridou
  • Ioannis Vlahavas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3746)

Abstract

The prediction of the Translation Initiation Site (TIS) in a genomic sequence is an important issue in biological research. Although several methods have been proposed to deal with this problem, there is a great potential for the improvement of the accuracy of these methods. Due to various reasons, including noise in the data as well as biological reasons, TIS prediction is still an open problem and definitely not a trivial task. In this paper we follow a three-step approach in order to increase TIS prediction accuracy. In the first step, we use a feature generation algorithm we developed. In the second step, all the candidate features, including some new ones generated by our algorithm, are ranked according to their impact to the accuracy of the prediction. Finally, in the third step, a classification model is built using a number of the top ranked features. We experiment with various feature sets, feature selection methods and classification algorithms, compare with alternative methods, draw important conclusions and propose improved models with respect to prediction accuracy.

Keywords

Initiation Codon Translation Initiation Site Amino Acid Pattern Correlation Base Feature Selection Adjusted Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • George Tzanis
    • 1
  • Christos Berberidis
    • 1
  • Anastasia Alexandridou
    • 1
  • Ioannis Vlahavas
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece

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