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NcPred for Accurate Nuclear Protein Prediction Using n-mer Statistics with Various Classification Algorithms

  • Md. Saiful Islam
  • Alaol Kabir
  • Kazi Sakib
  • Md. Alamgir Hossain
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)

Abstract

Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n − mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human protein, the accuracy is improved by about 25% and sensitivity about 10% with BF Tree. Performance analysis of NcPred clearly demonstrates its suitability over the contemporary in-silico nuclear protein classification research.

Keywords

Subcellular Localization Nuclear Protein Fanconis Anaemia Random Tree Alternate Decision 
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 2011

Authors and Affiliations

  • Md. Saiful Islam
    • 1
  • Alaol Kabir
    • 1
  • Kazi Sakib
    • 1
  • Md. Alamgir Hossain
    • 2
  1. 1.Institute of Information TechnologyUniversity of DhakaBangladesh
  2. 2.School of Computing, Engineering and Information ScienceNorthumbria UniversityUK

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