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IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 129–140Cite as

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A Machine Learning and Chemometrics Assisted Interpretation of Spectroscopic Data – A NMR-Based Metabolomics Platform for the Assessment of Brazilian Propolis

A Machine Learning and Chemometrics Assisted Interpretation of Spectroscopic Data – A NMR-Based Metabolomics Platform for the Assessment of Brazilian Propolis

  • Marcelo Maraschin23,25,
  • Amélia Somensi-Zeggio23,
  • Simone K. Oliveira23,
  • Shirley Kuhnen23,
  • Maíra M. Tomazzoli23,
  • Ana C. M. Zeri24,
  • Rafael Carreira25 &
  • …
  • Miguel Rocha25 
  • Conference paper
  • 1774 Accesses

  • 1 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, allowing to obtain relevant information as to the importance of a given set of features, mostly related to the structural fingerprint of aliphatic and aromatic compounds typically found in propolis, e.g., fatty acids and phenolic compounds. The feature selection and decision tree-based algorithms used appear to be suitable tools for building classification models for the Brazilian propolis metabolomics regarding its geographic origin, with consistency, high accuracy, and avoiding redundant information as to the metabolic signature of relevant compounds.

Keywords

  • Supervised classification techniques
  • evolutionary algorithms
  • Random Forest
  • PLS-DA
  • wrapper methods
  • NMR-based metabolomics

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References

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Author information

Authors and Affiliations

  1. Plant Morphogenesis and Biochemistry Laboratory, Federal University of Santa Catarina, Florianópolis, SC, Brazil

    Marcelo Maraschin, Amélia Somensi-Zeggio, Simone K. Oliveira, Shirley Kuhnen & Maíra M. Tomazzoli

  2. National Laboratory of Bioscience, Campinas, SP, Brazil

    Ana C. M. Zeri

  3. CCTC, School of Engineering, University of Minho, Campus Gualtar, Braga, Portugal

    Marcelo Maraschin, Rafael Carreira & Miguel Rocha

Authors
  1. Marcelo Maraschin
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  2. Amélia Somensi-Zeggio
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  3. Simone K. Oliveira
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  4. Shirley Kuhnen
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  5. Maíra M. Tomazzoli
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  6. Ana C. M. Zeri
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  7. Rafael Carreira
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  8. Miguel Rocha
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Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

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Cite this paper

Maraschin, M. et al. (2012). A Machine Learning and Chemometrics Assisted Interpretation of Spectroscopic Data – A NMR-Based Metabolomics Platform for the Assessment of Brazilian Propolis. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-34123-6_12

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