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Machine Learning Methods for the Protein Fold Recognition Problem

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Book cover Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 149 ))

Abstract

The protein fold recognition problem is crucial in bioinformatics. It is usually solved using sequence comparison methods but when proteins similar in structure share little in the way of sequence homology they fail and machine learning methods are used to predict the structure of the protein. The imbalance of the data sets, the number of outliers and the high number of classes make the task very complex. We try to explain the methodology for building classifiers for protein fold recognition and to cover all the major results in this field.

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Author information

Authors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Katarzyna Stapor & Piotr Fabian

  2. Jagiellonian University, Kraków, Poland

    Irena Roterman-Konieczna

Authors
  1. Katarzyna Stapor
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  2. Irena Roterman-Konieczna
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  3. Piotr Fabian
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Corresponding author

Correspondence to Katarzyna Stapor .

Editor information

Editors and Affiliations

  1. University of Piraeus , Piraeus, Greece

    George A. Tsihrintzis

  2. University of Piraeus , Piraeus, Greece

    Dionisios N. Sotiropoulos

  3. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, New South Wales, Australia

    Lakhmi C. Jain

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Stapor, K., Roterman-Konieczna, I., Fabian, P. (2019). Machine Learning Methods for the Protein Fold Recognition Problem. In: Tsihrintzis, G., Sotiropoulos, D., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 149 . Springer, Cham. https://doi.org/10.1007/978-3-319-94030-4_5

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