Automated structural classification of lipids by machine learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Oct 29, 2014

Abstract

MOTIVATION: Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms.

Authors

  • Ryan Taylor
    Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
  • Ryan H Miller
    Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
  • Ryan D Miller
    Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
  • Michael Porter
    Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
  • James Dalgleish
    Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
  • John T Prince
    Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.

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