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Prediction of Translation Initiation Sites Using Classifier Selection

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Advances in Artificial Intelligence (SETN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3955))

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Abstract

The prediction of the translation initiation site (TIS) in a genomic sequence is an important issue in biological research. Several methods have been proposed to deal with it. However, it is still an open problem. In this paper we follow an approach consisting of a number of steps in order to increase TIS prediction accuracy. First, all the sequences are scanned and the candidate TISs are detected. These sites are grouped according to the length of the sequence upstream and downstream them and a number of features is generated for each one. The features are evaluated among the instances of every group and a number of the top ranked ones are selected for building a classifier. A new instance is assigned to a group and is classified by the corresponding classifier. We experiment with various feature sets and classification algorithms, compare with alternative methods and draw important conclusions.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

    George Tzanis & Ioannis Vlahavas

Authors
  1. George Tzanis
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  2. Ioannis Vlahavas
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Editor information

Editors and Affiliations

  1. Computer Science Department of University of Crete, Greece

    Grigoris Antoniou

  2. Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Vassilika Vouton, P.O. Box 1385, 71110, Heraklion, Greece

    George Potamias

  3. Institute of Informatics and Telecommunications, NCSR "Demokritos", 15310 A., Paraskevi Attikis, Greece

    Costas Spyropoulos

  4. Institute of Computer Science, FO.R.T.H., Vassilika Vouton, P.O. Box 1385, GR 71110, Heraklion, Greece

    Dimitris Plexousakis

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© 2006 Springer-Verlag Berlin Heidelberg

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Tzanis, G., Vlahavas, I. (2006). Prediction of Translation Initiation Sites Using Classifier Selection. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_37

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  • DOI: https://doi.org/10.1007/11752912_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34117-8

  • Online ISBN: 978-3-540-34118-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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