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Evaluation of Descriptor Algorithms of Biological Sequences and Distance Measures for the Intelligent Cluster Index (ICIx)

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

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Abstract

In hindsight of the previous decades, a rapid growth of data in all fields of life sciences is perceptible. Most notably is the general tendency of retaining well established techniques regarding specific biological requirements and common taxonomies for data classification. Therefore a change in perspective towards advanced technological concepts for persisting, organizing and analyzing these huge amounts of data is essential. The Intelligent Cluster Index (ICIx) is a modern technology capable of indexing multidimensional data through semantic criteria, qualified for this challenge. In this paper methodical approaches for indexing biological sequences with the ICIx are discussed and evaluated. This includes the examination of established methods concentrating on vector transformation as well as outlining the efficiency of different distance measures applied to these vectors. Based on our results, it becomes apparent that position conserving methods are superior to other approaches and that the applied distance measures heavily influence performance and quality.

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Notes

  1. 1.

    Other commonly used notions for n-grams are k-, t- or n-tuples and k-, t- or n-mers.

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Acknowledgement

The study has been supported by the Free State of Saxony, the University of Applied Sciences Mittweida and Chemnitz University of Technology.

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Authors and Affiliations

  1. Department of Applied Computer Sciences and Biosciences, University of Applied Sciences Mittweida, Technikumplatz 17, 09648, Mittweida, Germany

    Stefan Schildbach, Florian Heinke & Dirk Labudde

  2. Department of Computer Science, Chemnitz University of Technology, Straße der Nationen 62, 09111, Chemnitz, Germany

    Stefan Schildbach & Wolfgang Benn

Authors
  1. Stefan Schildbach
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  2. Florian Heinke
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  3. Wolfgang Benn
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  4. Dirk Labudde
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Corresponding author

Correspondence to Stefan Schildbach .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Schildbach, S., Heinke, F., Benn, W., Labudde, D. (2016). Evaluation of Descriptor Algorithms of Biological Sequences and Distance Measures for the Intelligent Cluster Index (ICIx). In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_33

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

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