{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T12:54:46Z","timestamp":1762520086896},"reference-count":5,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,10,1]]},"abstract":"Abstract<\/jats:title>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 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.<\/jats:p>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.<\/jats:p>Availability and implementation: The MATLAB implementation of LICRE is available from https:\/\/sites.google.com\/site\/licrerepository\/<\/jats:p>Contact: \u00a0gerard.wong@bakeridi.edu.au or gerard.wong@unimelb.edu.au<\/jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu381","type":"journal-article","created":{"date-parts":[[2014,6,16]],"date-time":"2014-06-16T20:35:17Z","timestamp":1402950917000},"page":"2832-2833","source":"Crossref","is-referenced-by-count":15,"title":["LICRE<\/tt>: unsupervised feature correlation reduction for lipidomics"],"prefix":"10.1093","volume":"30","author":[{"given":"Gerard","family":"Wong","sequence":"first","affiliation":[{"name":"1 Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and 2 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia"},{"name":"1 Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and 2 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey","family":"Chan","sequence":"additional","affiliation":[{"name":"1 Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and 2 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bronwyn A.","family":"Kingwell","sequence":"additional","affiliation":[{"name":"1 Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and 2 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Leckie","sequence":"additional","affiliation":[{"name":"1 Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and 2 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter J.","family":"Meikle","sequence":"additional","affiliation":[{"name":"1 Baker IDI Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia and 2 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2014,6,13]]},"reference":[{"key":"2023041303472796400_","doi-asserted-by":"crossref","DOI":"10.1109\/IJCNN.2002.1007487","article-title":"VAT: a tool for visual assessment of (cluster) tendency","volume-title":"Proceedings of the 2002 International Joint Conference on Neural Networks, 2002. 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