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Identification of N-Glycosylation Sites with Sequence and Structural Features Employing Random Forests

  • Shreyas Karnik
  • Joydeep Mitra
  • Arunima Singh
  • B. D. Kulkarni
  • V. Sundarajan
  • V. K. Jayaraman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control.The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features.The best model has cross validation accuracy rate of 72.81%.

Keywords

Support Vector Machine Random Forest Glycosylation Site Amino Acid Property Contact Order 
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 2009

Authors and Affiliations

  • Shreyas Karnik
    • 1
    • 3
  • Joydeep Mitra
    • 1
  • Arunima Singh
    • 1
  • B. D. Kulkarni
    • 1
  • V. Sundarajan
    • 2
  • V. K. Jayaraman
    • 2
  1. 1.Chemical Engineering and Process Development DivisionNational Chemical LaboratoryPuneIndia
  2. 2.Center for Development of Advanced ComputingPune University CampusPuneIndia
  3. 3.School of InformaticsIndiana UniversityIndianapolisUSA

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