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dc.contributor.authorMartínez Velasco, Antonieta Teodora
dc.contributor.authorMartinez-Villaseñor, Lourdes
dc.contributor.authorMiralles, Luis
dc.contributor.authorPérez Ortíz, Andric Christopher
dc.contributor.authorEstrada, Francisco Javier
dc.coverage.spatialMéxico
dc.creatorMARÍA DE LOURDES GUADALUPE MARTÍNEZ VILLASEÑOR;241561
dc.creatorFRANCISCO JAVIER ESTRADA MENA;122973
dc.date.accessioned2017-10-24T22:58:11Z
dc.date.available2017-10-24T22:58:11Z
dc.date.issued2016
dc.identifier.citationMartínez Velasco, A. T.,Zenteno, J. C., Martínez Villaseñor, M. de L., Miralles Pechuan, L. J., Pérez Ortíz, A. C. y Estrada Mena, F. J. (2016). Machine learning method to establish the connection between age related macular degeneration and some genetic variations. En: García, C, Caballero Gil, P., Burmester, M. y Quesada Arencibia, A. (editores), Ubiquitous Computing and Ambient Intelligence : 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 - December 2, 2016 (vol. 2), (Lecture notes in computer science), (pp. 28-39). Cham : Springer International Publishing. DOI: 10.1007/978-3-319-48799-1_4
dc.identifier.isbn9783319487984
dc.identifier.issn0302-9743
dc.identifier.otherCampus Ciudad de México
dc.identifier.urihttps://hdl.handle.net/20.500.12552/916
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-48799-1_4
dc.description.abstractMedicine research based in machine learning methods allows the improvement of diagnosis in complex diseases. Age related Macular Degeneration (AMD) is one of them. AMD is the leading cause of blindness in the world. It causes the 8.7% of blind people. A set of case and controls study could be developed by machine-learning methods to find the relation between Single Nucleotide Polymorphisms (SNPs) SNP_A, SNP_B, SNP_C and AMD. In this paper we present a machine-learning based analysis to determine the relation of three single nucleotide SNPs and the AMD disease. The SNPs SNP_B, SNP_C remained in the top four relevant features with ophthalmologic surgeries and bilateral cataract. We aim also to determine the best set of features for the classification process. © Springer International Publishing AG 2016.
dc.description.statementofresponsibilityInvestigadores
dc.description.statementofresponsibilityEstudiantes
dc.description.statementofresponsibilityMaestros
dc.description.statementofresponsibilityPúblico en general
dc.description.tableofcontentsIngeniería
dc.description.tableofcontentsCiencias de la Salud
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relationVersión aceptada
dc.relation.ispartofREPOSITORIO SCRIPTA
dc.relation.ispartofREPOSITORIO NACIONAL CONACYT
dc.relation.ispartofOPENAIRE
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsAcceso Abierto
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0
dc.rights.urihttp://www.sherpa.ac.uk/romeo/issn/0302-9743/
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceUbiquitous Computing and Ambient Intelligence : 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 - December 2, 2016
dc.subjectMachine learning
dc.subjectMacular degeneration
dc.subjectPolymorphism relation
dc.subjectSingle nucleotide polymorphisms
dc.subject.classificationINGENIERÍA Y TECNOLOGÍA
dc.subject.classificationMEDICINA Y CIENCIAS DE LA SALUD
dc.titleMachine learning method to establish the connection between age related macular degeneration and some genetic variations
dc.typecontribución a congreso


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