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Data Augmentation Techniques to Improve Metabolomic Analysis in Niemann-Pick Type C Disease

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Computational Science – ICCS 2022 (ICCS 2022)

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Abstract

Niemann-Pick Class 1 (NPC1) disease is a rare and neurodegenerative disease, and often metabolomics datasets of NPC1 patients are limited in the number of samples and severely imbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence are employed to create additional synthetic samples. This paper presents DA techniques, based on the addition of noise, on oversampling techniques and using conditional generative adversarial networks, to evaluate their predictive capacities on a set of Nuclear Magnetic Resonance (NMR) profiles of urine samples. Prediction results obtained show increases in sensitivity (30%) and in F\(_{1}\) score (20%). In addition, multivariate data analysis and variable importance in projection scores have been applied. These analyses show the ability of the DA methods to replicate the information of the metabolites and determined that selected metabolites (such as 3-aminoisobutyrate, 3-hidroxivaleric, quinolinate and trimethylamine) may be valuable biomarkers for the diagnosis of NPC1 disease.

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Acknowledgements

The authors acknowledge the support from MICINN (Spain) through grant TIN2017-88728-C2-1-R and PID2020-116898RB-I00, from Universidad de Málaga y Junta de Andalucía through grant UMA20-FEDERJA-045, and from IBIMA (all including FEDER funds).

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Authors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática Universidad de Málaga, Málaga, Spain

    Francisco J. Moreno-Barea & Leonardo Franco

  2. School of Computer Science and Informatics, Faculty of Technology, De Montfort University, The Gateway, Leicester, UK

    David Elizondo

  3. Leicester School of Pharmacy, Faculty of Health and Life Sciences, De Montfort University, The Gateway, Leicester, UK

    Martin Grootveld

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  1. Francisco J. Moreno-Barea
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  2. Leonardo Franco
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  3. David Elizondo
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  4. Martin Grootveld
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Corresponding author

Correspondence to Francisco J. Moreno-Barea .

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Moreno-Barea, F.J., Franco, L., Elizondo, D., Grootveld, M. (2022). Data Augmentation Techniques to Improve Metabolomic Analysis in Niemann-Pick Type C Disease. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_8

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  • DOI: https://doi.org/10.1007/978-3-031-08757-8_8

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