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Translation Initiation Sites Prediction with Mixture Gaussian Models

  • Guoliang Li
  • Tze-Yun Leong
  • Louxin Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)

Abstract

Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features from cDNA sequences, such as the length of the open reading frame downstream from ATG, the number of other ATGs upstream and downstream from the current ATGs, etc. With these global features, the proposed method predicts TIS with sensitivity 98% and specificity 92%. The sensitivity is much better than that from other methods. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.

Keywords

Support Vector Machine Feature Vector Mixture Gaussian Model Global Feature Translation Initiation Site 
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 2004

Authors and Affiliations

  • Guoliang Li
    • 1
  • Tze-Yun Leong
    • 1
  • Louxin Zhang
    • 2
  1. 1.Medical Computing Laboratory, School of ComputingNational University of SingaporeSingapore
  2. 2.Department of MathematicsNational University of SingaporeSingapore

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