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Neural Feature Association Rule Mining for Protein Interaction Prediction

  • Jae-Hong Eom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

The prediction of protein interactions is an important problem in post–genomic biology. In this paper, we present an association rule mining method for protein interaction prediction. A neural network is used to cluster protein interaction data and a feature selection is used to reduce the dimension of protein features. For model training, the preliminary network model was constructed with existing protein interaction data in terms of their functional categories and interactions. A set of association rules for protein interaction prediction are derived by decoding a set of learned weights of trained neural network after this model training. The protein interaction data of Yeast from public databases are used. The prediction performance was compared with simple association rule-based approach. According to the experimental results, proposed method achieved about 95.5% accuracy.

Keywords

Association Rule Association Rule Mining Trained Neural Network Adaptive Resonance Theory Adaptive Neural Network 
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

  • Jae-Hong Eom
    • 1
  1. 1.Biointelligence Laboratory, School of Computer Science and EngineeringSeoul National UniversitySeoulSouth Korea

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