Identifying Transcription Factor Binding Sites Based on a Neural Network

  • Zhiming Dai
  • Xianhua Dai
  • Jiang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


The identification of regulatory motifs (transcription factor binding sites) in DNA sequences is a difficult pattern recognition problem. Many methods have been developed in the past few years. Although some are better than the others in a sense, yet not a single one is recognized to be the best. Generally, in the case of long and subtle motifs, exhaustive enumeration becomes problematic. In this paper,we present a new method which improves exhaustive enumeration based on a neural network. We test its performance on both synthetic data and realistic biological data. It proved to be successful in identifying very subtle motifs. Experiments also show our method outperforms some popular methods in terms of identifying subtle motifs. We refer to the new method as IMNN (Identifying Motifs based on a Neural Network).


Hide Layer Transcription Factor Binding Site Synthetic Data Consensus Motif Motif Problem 
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 2006

Authors and Affiliations

  • Zhiming Dai
    • 1
  • Xianhua Dai
    • 1
  • Jiang Wang
    • 1
  1. 1.School of Information Science and TechnologySun Yat-sen UniversityGuangzhouP.R. China

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