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Metabolites Selection and Classification of Metabolomics Data on Alzheimer’s Disease Using Random Forest

  • Mohammad Nasir AbdullahEmail author
  • Bee Wah Yap
  • Yuslina Zakaria
  • Abu Bakar Abdul Majeed
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 652)

Abstract

Alzheimer’s disease (AD) is neurodegenerative disorder characterized by the gradual memory loss, impairment of cognitive functions and progressive disability. It is known from previous studies that symptoms of AD are due to synaptic dysfunction and neuronal death in the area of the brain, which performs memory consolidation. Thus, the investigation of deviations in various cellular metabolite linkages is crucial to advance our understanding of early disease mechanism and to identify novel therapeutic targets. This study aims to identify small sets of metabolites that could be potential biomarkers of AD. Liquid chromatography/mass spectrometry-quadrupole time of flight (LC/MS-QTOF)-based metabolomics data were used to determine potential biomarkers. The metabolic profiling detected a total of 100 metabolites for 55 AD patients and 55 healthy control. Random forest (RF), a supervised classification algorithm was used to select the important features that might elucidate biomarkers of AD. Mean decrease accuracy of .05 or higher indicates important variables. Out of 100 metabolites, 10 were significantly modified, namely N-(2-hydroxyethyl) icosanamide which had the highest Gini index followed by X11-12-dihyroxy (arachidic) acid, N-(2-hydroxyethyl) palmitamide, phytosphingosine, dihydrosphingosine, deschlorobenzoyl indomenthacin, XZN-2-hydroxyethyl (icos) 11-enamide, X1-hexadecanoyl (sn) glycerol, trypthophan and dihydroceramide C2.

Keywords

Alzheimer’s disease Biomarkers Metabolite Random forest 

Notes

Acknowledgement

This work is supported by the Ministry of Higher Education (MOHE) under Long Term Research Grant Scheme (Reference no: 600-RMI/LRGS 5/3 [3/2012]). The authors would like to thank Universiti Teknologi MARA (UiTM) for supporting this research. Lastly, we would like to thank Che Adlia Enche Ady at Collaborative Drug Discovery Research (CDDR) Group, Faculty of Pharmacy, Universiti Teknologi MARA for her technical help and generosity in providing the metabolomics data.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Mohammad Nasir Abdullah
    • 1
    Email author
  • Bee Wah Yap
    • 2
  • Yuslina Zakaria
    • 3
  • Abu Bakar Abdul Majeed
    • 3
    • 4
  1. 1.Department of Statistics, Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARATapahMalaysia
  2. 2.Advanced Analytic Engineering Centre, Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia
  3. 3.Faculty of PharmacyUniversiti Teknologi MARABandar Puncak AlamMalaysia
  4. 4.Brain Degeneration and Therapeutics Group, Pharmaceutical and Life Science Community of ResearchUniversiti Teknologi MARAShah AlamMalaysia

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