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Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices

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Prediction of Protein Secondary Structure

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1484))

Abstract

Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.

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Notes

  1. 1.

    Less correct name since jacknife is a resampling method rather than a cross-validation type.

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Acknowledgements

Marcin Tatjewski was supported by the European Union from resources of the European Social Fund. Project PO KL “Information technologies: Research and their interdisciplinary applications”, Agreement UDA-POKL.04.01.01-00-051/10-00. Marcin Tatjewski and Dariusz Plewczynski were supported by Polish National Science Centre (grant numbers: 2015/16/T/ST6/00493, 2014/15/B/ST6/05082 and 2013/09/B/NZ2/00121) and EU COST BM1405 and BM1408 actions. Marcin Kierczak was supported by the Swedish Foundation for Strategic Research and the Swedish Research Council.

Author information

Authors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Marcin Tatjewski

  2. Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097, Warsaw, Poland

    Marcin Tatjewski

  3. Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden

    Marcin Kierczak

  4. Centre of New Technologies, University of Warsaw, S. Banacha 2c, Warsaw, 02-097, Poland

    Dariusz Plewczynski

Authors
  1. Marcin Tatjewski
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  2. Marcin Kierczak
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  3. Dariusz Plewczynski
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Correspondence to Dariusz Plewczynski .

Editor information

Editors and Affiliations

  1. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, Australia

    Yaoqi Zhou

  2. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USA

    Andrzej Kloczkowski

  3. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA

    Eshel Faraggi

  4. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, Australia

    Yuedong Yang

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Tatjewski, M., Kierczak, M., Plewczynski, D. (2017). Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices. In: Zhou, Y., Kloczkowski, A., Faraggi, E., Yang, Y. (eds) Prediction of Protein Secondary Structure. Methods in Molecular Biology, vol 1484. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6406-2_19

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  • DOI: https://doi.org/10.1007/978-1-4939-6406-2_19

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6404-8

  • Online ISBN: 978-1-4939-6406-2

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