title: LICRE: unsupervised feature correlation reduction for lipidomics.
abstract: BACKGROUND
  Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with disease risk, progression and treatment response. Clinically, these lipid biomarkers can be used to construct classification models for the purpose of disease screening or diagnosis. However, the inclusion of a large number of highly correlated biomarkers within a model may reduce classification performance, unnecessarily inflate associated costs of a diagnosis or a screen and reduce the feasibility of clinical translation. An unsupervised feature reduction approach can reduce feature redundancy in lipidomic biomarkers by limiting the number of highly correlated lipids while retaining informative features to achieve good classification performance for various clinical outcomes. Good predictive models based on a reduced number of biomarkers are also more cost effective and feasible from a clinical translation perspective.
  
  
  RESULTS
  The application of LICRE to various lipidomic datasets in diabetes and cardiovascular disease demonstrated superior discrimination in terms of the area under the receiver operator characteristic curve while using fewer lipid markers when predicting various clinical outcomes.
  
  
  METHODS
  The MATLAB implementation of LICRE is available from http://ww2.cs.mu.oz.au/∼gwong/LICRE
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.
authors.forename: Gerard
authors.initials: G
authors.lastname: Wong
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Chan
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.
authors.forename: Bronwyn A
authors.initials: BA
authors.lastname: Kingwell
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.
authors.forename: Christopher
authors.initials: C
authors.lastname: Leckie
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Meikle
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000378:metabolism; D002853:Chromatography, Liquid / Q000379:methods*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D003920:Diabetes Mellitus / Q000378:metabolism; D006801:Humans; D008055:Lipids / Q000737:chemistry*; D012372:ROC Curve; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D015415:Biomarkers; D008055:Lipids