LICRE: Unsupervised feature correlation reduction for lipidomics

G Wong; J Chan; BA Kingwell; C Leckie; PJ Meikle

Journal article

LICRE: Unsupervised feature correlation reduction for lipidomics

G Wong, J Chan, BA Kingwell, C Leckie, PJ Meikle

Bioinformatics | OXFORD UNIV PRESS | Published : 2014

DOI: 10.1093/bioinformatics/btu381

Abstract

Motivation: 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 la..

View full abstract

University of Melbourne Researchers

Bronwyn Kingwell Author

Christopher Leckie Author

Peter Meikle Author

Related Projects (1)

Next Generation Data Mining Techniques for Analysing Large Evolving Networks

In order to understand complex systems such as the Internet or gene interactions, we need to analyse how the networks in these systems funct..

Grants

Awarded by National Institute of Diabetes and Digestive and Kidney Diseases

Funding Acknowledgements

Funding: This work was supported by funding from the Dairy Health and Nutrition Consortium, the National Health and Medical Research Council of Australia, the OIS Program of the Victorian Government, Australia and by Award Number 1R01DK088972-01 from the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, USA and the Australian Research Council's Discovery Projects funding scheme (project number DP110102621).