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dc.contributor.author | Quintás, Guillermo![]() |
es_ES |
dc.contributor.author | Portillo Poblador, Nuria![]() |
es_ES |
dc.contributor.author | García Cañaveras, Juan Carlos![]() |
es_ES |
dc.contributor.author | Castell, José Vicente![]() |
es_ES |
dc.contributor.author | Ferrer, Alberto![]() |
es_ES |
dc.contributor.author | Lahoz, Agustín![]() |
es_ES |
dc.date.accessioned | 2016-03-23T15:35:21Z | |
dc.date.available | 2016-03-23T15:35:21Z | |
dc.date.issued | 2012-02 | |
dc.identifier.issn | 1573-3882 | |
dc.identifier.uri | http://hdl.handle.net/10251/62036 | |
dc.description | The online version of this article (doi:10.1007/s11306-011-0292-5) contains supplementary material, which is available to authorized users. | es_ES |
dc.description.abstract | An MS-based metabolomics strategy including variable selection and PLSDA analysis has been assessed as a tool to discriminate between non-steatotic and steatotic human liver profiles. Different chemometric approaches for uninformative variable elimination were performed by using two of the most common software packages employed in the field of metabolomics (i.e., MATLAB and SIMCA-P). The first considered approach was performed with MATLAB where the PLS regression vector coefficient values were used to classify variables as informative or not. The second approach was run under SIMCA-P, where variable selection was performed according to both the PLS regression vector coefficients and VIP scores. PLSDA models performance features, such as model validation, variable selection criteria, and potential biomarker output, were assessed for comparison purposes. One interesting finding is that variable selection improved the classification predictiveness of all the models by facilitating metabolite identification and providing enhanced insight into the metabolic information acquired by the UPLC-MS method. The results prove that the proposed strategy is a potentially straightforward approach to improve model performance. Among others, GSH, lysophospholipids and bile acids were found to be the most important altered metabolites in the metabolomic profiles studied. However, further research and more in-depth biochemical interpretations are needed to unambiguously propose them as disease biomarkers. | es_ES |
dc.description.sponsorship | This work has been supported by Conselleria de Sanitat (Regional Valencian Ministry of Health) contract (AP-193/10). A. L is grateful for a Miguel Servet contract (CP08/00125) from the Spanish Ministry of Science and Innovation/Instituto de Salud Carlos III. J.C G-C is grateful for a pre-doctoral contract from the val I + d program of the Conselleria d'Educacio (Regional Valencian Ministry of Education). | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Springer Verlag (Germany) | es_ES |
dc.relation.ispartof | Metabolomics | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Mass spectrometry | es_ES |
dc.subject | Metabolomics | es_ES |
dc.subject | PLSDA and steatosis | es_ES |
dc.subject | Bile acid | es_ES |
dc.subject | Biological marker | es_ES |
dc.subject | Glutathione | es_ES |
dc.subject | Lysophospholipid | es_ES |
dc.subject | Accuracy | es_ES |
dc.subject | Adult | es_ES |
dc.subject | Article | es_ES |
dc.subject | Chemometric analysis | es_ES |
dc.subject | Computer program | es_ES |
dc.subject | Controlled study | es_ES |
dc.subject | Discriminant analysis | es_ES |
dc.subject | Disease marker | es_ES |
dc.subject | External validity | es_ES |
dc.subject | Female | es_ES |
dc.subject | Human | es_ES |
dc.subject | Human tissue | es_ES |
dc.subject | Male | es_ES |
dc.subject | Metabolite | es_ES |
dc.subject | Nonalcoholic fatty liver | es_ES |
dc.subject | Partial least squares discriminant analysis | es_ES |
dc.subject | Principal component analysis | es_ES |
dc.subject | Reproducibility | es_ES |
dc.subject | Signal noise ratio | es_ES |
dc.subject | Standardization | es_ES |
dc.subject | Time of flight mass spectrometry | es_ES |
dc.subject | Ultra performance liquid chromatography | es_ES |
dc.subject | Validation process | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s11306-011-0292-5 | |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//AP-193%2F10/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//CP08%2F00125/ES/CP08%2F00125/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Quintás, G.; Portillo Poblador, N.; García Cañaveras, JC.; Castell, JV.; Ferrer, A.; Lahoz, A. (2012). Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool. Metabolomics. 8(1):86-98. https://doi.org/10.1007/s11306-011-0292-5 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1007/s11306-011-0292-5 | es_ES |
dc.description.upvformatpinicio | 86 | es_ES |
dc.description.upvformatpfin | 98 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 8 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 202369 | es_ES |
dc.identifier.eissn | 1573-3890 | |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
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