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Model simplification for supervised classification of metabolic networks

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Abstract

Many real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical models to classify them, based on various graph distances. Due to the complexity of the models, made of tens of thousands of nodes and edges, we focus on model simplification solutions to reduce execution times, still maintaining high accuracy. Experimental results on three datasets of biological interest show the achieved performance improvements.

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Acknowledgements

The work was carried out also within the activities of M.R.G. and L.M. as members of the INdAM Research group GNCS. The authors would like to thank G. Trerotola for the technical support.

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Authors and Affiliations

  1. ICAR, National Research Council of Italy, Naples, Italy

    Ilaria Granata, Mario R. Guarracino, Lucia Maddalena & Ichcha Manipur

  2. LATNA, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Valery A. Kalyagin

  3. CAO, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

Authors
  1. Ilaria Granata
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  2. Mario R. Guarracino
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  3. Valery A. Kalyagin
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  4. Lucia Maddalena
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  5. Ichcha Manipur
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  6. Panos M. Pardalos
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Correspondence to Mario R. Guarracino.

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This work was supported by the PON MISE project BigImaging and by the National Research University Higher School of Economics, RSF grant n. 14-41-00039.

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Granata, I., Guarracino, M.R., Kalyagin, V.A. et al. Model simplification for supervised classification of metabolic networks. Ann Math Artif Intell 88, 91–104 (2020). https://doi.org/10.1007/s10472-019-09640-y

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Keywords

Mathematics Subject Classification (2010)

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