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dc.contributor.author | García Gómez, Juan Miguel | es_ES |
dc.contributor.author | Gómez-Sanchis, Juan | es_ES |
dc.contributor.author | Escandell-Montero, Pablo | es_ES |
dc.contributor.author | Fuster García, Elíes | es_ES |
dc.contributor.author | Soria-Olivas, Emilio | es_ES |
dc.date.accessioned | 2014-10-14T11:34:38Z | |
dc.date.available | 2014-10-14T11:34:38Z | |
dc.date.issued | 2013-11 | |
dc.identifier.issn | 0010-4825 | |
dc.identifier.uri | http://hdl.handle.net/10251/43245 | |
dc.description.abstract | Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single-labeling cDNA microarrays with 11500 original clones. Afterwards, supervised and unsupervised methodologies were applied to obtain the classification model: the former was based on linear discriminant analysis, the later on clustering using the SMCE embedding data. The results obtained using both approaches showed that all (100%) the samples could be correctly classified and the results of all repetitions but one formed a compatible cluster of predictive labels. Finally, the embedding dimensionality of the dataset extracted by SMCE revealed large discrimination margins between both classes. (c) 2013 Elsevier Ltd. All rights reserved. | es_ES |
dc.description.sponsorship | This work was supported by the University of Valencia through project UV-INV-AE11-41271. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computers in Biology and Medicine | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Manifolds | es_ES |
dc.subject | Automatic classification | es_ES |
dc.subject | Microarray data analysis | es_ES |
dc.subject | Bioinformatics | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.title | Sparse Manifold Clustering and Embedding to discriminategene expression profiles of glioblastoma and meningioma tumors | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.compbiomed.2013.08.025 | |
dc.relation.projectID | info:eu-repo/grantAgreement/UV//UV-INV-AE11-41271/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació | es_ES |
dc.description.bibliographicCitation | García Gómez, JM.; Gómez-Sanchis, J.; Escandell-Montero, P.; Fuster García, E.; Soria-Olivas, E. (2013). Sparse Manifold Clustering and Embedding to discriminategene expression profiles of glioblastoma and meningioma tumors. Computers in Biology and Medicine. 43(11):1863-1869. https://doi.org/10.1016/j.compbiomed.2013.08.025 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.compbiomed.2013.08.025 | es_ES |
dc.description.upvformatpinicio | 1863 | es_ES |
dc.description.upvformatpfin | 1869 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 43 | es_ES |
dc.description.issue | 11 | es_ES |
dc.relation.senia | 249015 | |
dc.contributor.funder | Universitat de València | es_ES |