Prediction of Transcription Factor Families Using DNA Sequence Features

  • Ashish Anand
  • Gary B. Fogel
  • Ganesan Pugalenthi
  • P. N. Suganthan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)

Abstract

Understanding the mechanisms of protein-DNA interaction is of critical importance in biology. Transcription factor (TF) binding to a specific DNA sequence depends on at least two factors: A protein-level DNA-binding domain and a nucleotide-level specific sequence serving as a TF binding site. TFs have been classified into families based on these factors. TFs within each family bind to specific nucleotide sequences in a very similar fashion. Identification of the TF family that might bind at a particular nucleotide sequence requires a machine learning approach. Here we considered two sets of features based on DNA sequences and their physicochemical properties and applied a one-versus-all SVM (OVA-SVM) with class-wise optimized features to identify TF family-specific features in DNA sequences. Using this approach, a mean prediction accuracy of ~80% was achieved, which represents an improvement of ~7% over previous approaches on the same data.

Keywords

Transcription factor family prediction multi-class classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ashish Anand
    • 1
  • Gary B. Fogel
    • 2
  • Ganesan Pugalenthi
    • 1
  • P. N. Suganthan
    • 1
  1. 1.School of Electrical and Electonic EngineeringNanyang Technological UniversitySingapore
  2. 2.Natural SelectionSan Diego

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