A Weighted Profile Based Method for Protein-RNA Interacting Residue Prediction

  • Euna Jeong
  • Satoru Miyano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3939)


The prediction of putative RNA-interacting residues in proteins is an important problem in a field of molecular recognition. We suggest a weighted profile based method for predicting RNA-interacting residues, which utilizes the trained neural network. Most neural networks have a learning rule which allows the network to adjust its connection weights in order to correctly classify the training data. We focus on the network weights that are dependent on the training data set and give evidence of which inputs were more influential in the network. A large set of the network weights trained on sequence profiles is analyzed and qualified. We explore the feasibility of utilizing the qualified information to improve the prediction performance for protein-RNA interaction. Our proposed method shows a considerable improvement, which has been applied to the profiles of the PSI-BLAST alignment. Results for predictions using alternative representations of profile are included for comparison.


Hide Markov Model Network Weight Amino Acid Type Saliency Factor Trained 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

  • Euna Jeong
    • 1
  • Satoru Miyano
    • 1
  1. 1.Human Genome Center, Institute of Medical ScienceUniversity of TokyoTokyoJapan

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