title: Automated structural classification of lipids by machine learning.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification.
  
  
  METHODS
  Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier.
authors.affiliation: Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
authors.forename: Ryan
authors.initials: R
authors.lastname: Taylor
authors.affiliation: Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
authors.forename: Ryan H
authors.initials: RH
authors.lastname: Miller
authors.affiliation: Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
authors.forename: Ryan D
authors.initials: RD
authors.lastname: Miller
authors.affiliation: Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Porter
authors.affiliation: Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
authors.forename: James
authors.initials: J
authors.lastname: Dalgleish
authors.affiliation: Department of Chemistry and Biochemistry and Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA.
authors.forename: John T
authors.initials: JT
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation / Q000379:methods*; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D008055:Lipids / Q000737:chemistry* / Q000145:classification*; D011381:Programming Languages
chemical_list: D008055:Lipids