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Predicting Protein-Protein Interactions with K-Nearest Neighbors Classification Algorithm

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6160))

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Abstract

In this work we address the problem of predicting protein-protein interactions. Its solution can give greater insight in the study of complex diseases, like cancer, and provides valuable information in the study of active small molecules for new drugs, limiting the number of molecules to be tested in laboratory. We model the problem as a binary classification task, using a suitable coding of the amino acid sequences. We apply k-Nearest Neighbors classification algorithm to the classes of interacting and noninteracting proteins. Results show that it is possible to achieve high prediction accuracy in cross validation. A case study is analyzed to show it is possible to reconstruct a real network of thousands interacting proteins with high accuracy on standard hardware.

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Authors and Affiliations

  1. High Performance Computing and Networking Institute (ICAR-CNR), National Research Council, Italy, Via P. Castellino, 111, 80131, Napoli, (IT)

    Mario R. Guarracino & Adriano Nebbia

Authors
  1. Mario R. Guarracino
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  2. Adriano Nebbia
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Editors and Affiliations

  1. DISI - Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Francesco Masulli

  2. Center for Biostatistics, The Methodist Hospital Research Institute (TMHRI), Weill Cornell Medical College, Cornell University, 6565 Fannin, Suite MGJ6-031, 77030, Houston, Texas, USA

    Leif E. Peterson

  3. Dipartimento di Matematica ed Informatica, Università di Salerno, Via Ponte don Melillo, 84084, Fisciano, (Sa), Italy

    Roberto Tagliaferri

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Guarracino, M.R., Nebbia, A. (2010). Predicting Protein-Protein Interactions with K-Nearest Neighbors Classification Algorithm. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2009. Lecture Notes in Computer Science(), vol 6160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14571-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-14571-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14570-4

  • Online ISBN: 978-3-642-14571-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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