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Machine-Learning Methods to Predict Protein Interaction Sites in Folded Proteins

  • Conference paper
Book cover Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7548))

Abstract

A reliable predictor of protein-protein interaction sites is necessary to investigate and model protein functional interaction networks. Hidden Markov Support Vector Machines (HM-SVM) have been shown to be among the best performing methods on this task. Furthermore, it has been noted that the performance of a predictor improves when its input takes advantage of the difference between observed and predicted residue solvent accessibility. In this paper, for first time, we combine these elements and we present ISPRED2, a new HM-SVM-based method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43). ISPRED2 consists of a sets of Python scripts aimed at integrating the different third-party software to obtain the final prediction.

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Author information

Authors and Affiliations

  1. Biocomputing Group, University of Bologna, via Irnerio 42, 40126, Bologna, Italy

    Castrense Savojardo, Piero Fariselli, Damiano Piovesan, Pier Luigi Martelli & Rita Casadio

  2. Department of Computer Science, University of Bologna, via Mura Anteo Zamboni 7, 40127, Bologna, Italy

    Castrense Savojardo & Piero Fariselli

Authors
  1. Castrense Savojardo
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  2. Piero Fariselli
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  3. Damiano Piovesan
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  4. Pier Luigi Martelli
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  5. Rita Casadio
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Editor information

Editors and Affiliations

  1. Department of Clinical Sciences and Community, University of Milan, via Vanzetti, 5, 20133, Milan, Italy

    Elia Biganzoli  & Federico Ambrogi  & 

  2. Dpto. de Lenguajes y Sistemas Informáticos (LSI), Universidad Politecnica de la Catalunya, C/Jordi Girona 1-3, Ed. Omega, 08034, Barcelona, Spain

    Alfredo Vellido

  3. Department of Mathematics and Information Sciences, University of Salerno, Via Ponte don Melillo, 84084, Fisciano, SA, Italy

    Roberto Tagliaferri

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© 2012 Springer-Verlag Berlin Heidelberg

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Savojardo, C., Fariselli, P., Piovesan, D., Martelli, P.L., Casadio, R. (2012). Machine-Learning Methods to Predict Protein Interaction Sites in Folded Proteins. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-35686-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35685-8

  • Online ISBN: 978-3-642-35686-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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