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International Workshop on Self-Organizing Maps

WSOM 2019: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization pp 324–333Cite as

Searching for the Origins of Life – Detecting RNA Life Signatures Using Learning Vector Quantization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 976))

Abstract

The most plausible hypothesis for explaining the origins of life on earth is the RNA world hypothesis supported by a growing number of research results from various scientific areas. Frequently, the existence of a hypothetical species on earth is supposed, with a base RNA sequence probably dissimilar from any known genomes today. It is hard to distinguish hypothetical sequences obtained by computer simulations from biological sequences and, hence, to decide which characteristics provide biological functionality. In the present consideration biological sequences obtained from RNA-viruses are compared with computationally generated sequences (artificial life probes). The task is to discriminate the samples regarding their origin, biological or artificial. We used the learning vector quantization (LVQ) model as the respective classifier. LVQ is a dissimilarity based classifier, which has only weak requirements regarding the underlying dissimilarity measure. This gives the opportunity to investigate several dissimilarity measures regarding their discriminating behavior for this task. Particularly, we consider information theoretic dissimilarities like the normalized compression distance (NCD) and divergences based on bag-of-word (BoW) vectors generated on the base of nucleotide-codons. Additionally, the geodesic path distance is applied taking an unary coding of sequences for a representation in the underlying Grassmann-manifold. Both, BoW and GPD allow continuous updates of prototypes in the feature space and in the Grassmann-manifold, respectively, whereas NCD restricts the application of LVQ methods to median variants.

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Notes

  1. 1.

    For computational convenience it is usually assumed that both matrices \(\mathbf {X}\) and \(\mathbf {Y}\) are orthonormal, which can always be obtained by Gram-Schmidt-orthonormalization. We will take this assumption here, too. If this assumption is dropped the procedure is still valid but more complicated. We refer to [34].

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Acknowledgement

M.K. was supported by grants of the European Social Fond (ESF) for a Young Researcher Group ‘MACS’ in cooperation with the TU Bergakademie Freiberg (Germany) and for the project titled ‘Digitale Produkt- und Prozessinovationen’ at the UAS Mittweida.

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Authors and Affiliations

  1. Saxony Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, Mittweida, Germany

    Thomas Villmann, Marika Kaden & Andrea Villmann

  2. Institute of Computing Science, Poznan University of Technology, Poznan, Poland

    Szymon Wasik, Mateusz Kudla, Kaja Gutowska & Jacek Blazewicz

  3. Institute of Bioorganic Chemistry, Polish Academy of Sciences, Warsaw, Poland

    Kaja Gutowska & Jacek Blazewicz

  4. Schulzentrum Döbeln-Mittweida, Döbeln, Germany

    Andrea Villmann

Authors
  1. Thomas Villmann
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  2. Marika Kaden
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  3. Szymon Wasik
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  4. Mateusz Kudla
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  5. Kaja Gutowska
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  6. Andrea Villmann
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  7. Jacek Blazewicz
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Corresponding author

Correspondence to Thomas Villmann .

Editor information

Editors and Affiliations

  1. Department of Computer Science, UPC BarcelonaTech, Barcelona, Spain

    Alfredo Vellido

  2. Knowledge Engineering and Machine Learning Group (KEMLG) at Intelligent Data Science and Artificial Intelligence Research Center, UPC BarcelonaTech, Barcelona, Spain

    Karina Gibert

  3. Department of Automatic Control, UPC BarcelonaTech, Barcelona, Spain

    Cecilio Angulo

  4. Departament d'Enginyeria Electrònica, Universitat de València, Burjassot, Valencia, Spain

    José David Martín Guerrero

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Villmann, T. et al. (2020). Searching for the Origins of Life – Detecting RNA Life Signatures Using Learning Vector Quantization. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_32

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