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Machine Learning Study of DNA Binding by Transcription Factors from the LacI Family

  • Gennady G. Fedonin
  • Mikhail S. Gelfand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6282)

Abstract

We studied 1372 LacI-family transcription factors and their 4484 DNA binding sites using machine learning algorithms and feature selection techniques. The Naive Bayes classifier and Logistic Regression were used to predict binding sites given transcription factor sequences. Prediction accuracy was estimated using 10-fold cross-validation. Experiments showed that the best prediction of nucleotide densities at selected site positions is obtained using only a few key protein sequence positions. These positions are stably selected by the forward feature selection based on the mutual information of factor-site position pairs.

Keywords

transcription factors naive Bayes classifier logistic regression mutual information 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gennady G. Fedonin
    • 1
  • Mikhail S. Gelfand
    • 1
  1. 1.Institute for Information Transmission Problems (the Kharkevich Institute)RASMoscowRussia

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