METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance.

Journal: Bioinformatics (Oxford, England)
Published Date: Nov 30, 2022

Abstract

MOTIVATION: Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms.

Authors

  • Emily Hashimoto-Roth
    Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Ottawa, ON, Canada.
  • Anuradha Surendra
    Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada.
  • Mathieu LavallĂ©e-Adam
    Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada.
  • Steffany A L Bennett
    Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Ottawa, ON, Canada.
  • Miroslava Cuperlovic-Culf
    Digital Technologies Research Center, National Research Council Canada, Ottawa, ON, Canada.

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