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dc.contributor.authorRuiz-Rodado, Victoren
dc.contributor.authorLuque-Baena, R. M.en
dc.contributor.authorte Vruchte, D. J.en
dc.contributor.authorProbert, Fayen
dc.contributor.authorLachmann, R. H.en
dc.contributor.authorHendriksz, Christian J.en
dc.contributor.authorWraith, James E.en
dc.contributor.authorImrie, Jackieen
dc.contributor.authorElizondo, Daviden
dc.contributor.authorSillence, Daniel J.en
dc.contributor.authorClayton, P.en
dc.contributor.authorPlatt, Frances M.en
dc.contributor.authorGrootveld, M.en
dc.date.accessioned2015-03-16T15:27:22Z
dc.date.available2015-03-16T15:27:22Z
dc.date.issued2014-11-14
dc.identifier.citationRuiz-Rodado, V. et al. (2014) 1H NMR-Linked Urinary Metabolic Profiling of Niemann-Pick Class C1 (NPC1) Disease: Identification of Potential New Biomarkers using Correlated Component Regression (CCR) and Genetic Algorithm (GA) Analysis Strategies. Current Metabolomics, 2 (2), pp. 88-121en
dc.identifier.issn2213-2368
dc.identifier.urihttp://hdl.handle.net/2086/10780
dc.description.abstractNiemann-Pick Class 1 (NPC1) disease is a rare, debilitating neurodegenerative lysosomal storage disease; however, urinary biomarkers available for it and its prognosis are currently limited. In order to identify and establish such biomarkers, we employed high-resolution 1H NMR analysis coupled to a range of multivariate (MV) analysis approaches, i.e. PLS-DA, RFs and uniquely the cross-validated correlated component regression (CCR) strategy in order to discern differences between the urinary metabolic profiles of 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Novel computational intelligence techniques (CITs) involving genetic algorithms (GAs) were also employed for this purposeen
dc.language.isoenen
dc.publisherBentham Scienceen
dc.title1H NMR-Linked Urinary Metabolic Profiling of Niemann-Pick Class C1 (NPC1) Disease: Identification of Potential New Biomarkers using Correlated Component Regression (CCR) and Genetic Algorithm (GA) Analysis Strategiesen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.2174/2213235X02666141112215616
dc.researchgroupPharmacologyen
dc.peerreviewedYesen
dc.funderSPARKS UKen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteLeicester Institute for Pharmaceutical Innovation - From Molecules to Practice (LIPI)en


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