An Ensemble Spectral Prediction (ESP) model for metabolite annotation.

Journal: Bioinformatics (Oxford, England)
PMID:

Abstract

MOTIVATION: A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule.

Authors

  • Xinmeng Li
    Department of Computer Science, Tufts University, Massachusetts, United States of America.
  • Yan Zhou Chen
    Department of Computer Science, Tufts University, Medford, MA, 02155, United States.
  • Apurva Kalia
    Department of Computer Science, Tufts University, Medford, MA, 02155, United States.
  • Hao Zhu
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology Wuhan 430070 PR China chang@whut.edu.cn suntl@whut.edu.cn.
  • Li-Ping Liu
    College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, China.
  • Soha Hassoun
    Department of Computer Science, Tufts University, Massachusetts, United States of America.