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

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Transactions on Computational Systems Biology IV

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 3939))

Abstract

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.

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Author information

Authors and Affiliations

  1. Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, 108-8639, Japan

    Euna Jeong & Satoru Miyano

Authors
  1. Euna Jeong
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  2. Satoru Miyano
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Editor information

Editors and Affiliations

  1. Centre for Computational and Systems Biology, The Microsoft Research - University of Trento, Piazza Manci, 17, 38050, Povo, (TN), Italy

    Corrado Priami

  2. Microsoft Research, UK

    Luca Cardelli

  3. Microsoft Research Cambridge, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK

    Stephen Emmott

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© 2006 Springer-Verlag Berlin Heidelberg

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Jeong, E., Miyano, S. (2006). A Weighted Profile Based Method for Protein-RNA Interacting Residue Prediction. In: Priami, C., Cardelli, L., Emmott, S. (eds) Transactions on Computational Systems Biology IV. Lecture Notes in Computer Science(), vol 3939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732488_11

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  • DOI: https://doi.org/10.1007/11732488_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33245-9

  • Online ISBN: 978-3-540-33248-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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