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A methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networks

dc.contributor.authorSánchez Lasheras, Juan Enrique
dc.contributor.authorGonzález Donquiles, Carmen
dc.contributor.authorGarcía Nieto, Paulino José 
dc.contributor.authorJiménez Moleón, José Juan
dc.contributor.authorSalas, Dolores
dc.contributor.authorSuárez Gómez, Sergio Luis 
dc.contributor.authorMolina de la Torre, Antonio José
dc.contributor.authorGonzález-Nuevo González, Joaquín 
dc.contributor.authorBonavera, Laura 
dc.contributor.authorCarballido Landeira, Jorge 
dc.contributor.authorCos Juez, Francisco Javier de 
dc.date.accessioned2018-10-08T10:23:36Z
dc.date.available2018-10-08T10:23:36Z
dc.date.issued2018
dc.identifier.citationNeural Computing and Applications, p. 1-8 (2018); doi:10.1007/s00521-018-3503-4
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/10651/48932
dc.format.extentp. 1-8
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.rights© The Natural Computing Applications Forum 2018
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046492371&doi=10.1007%2fs00521-018-3503-4&partnerID=40&md5=d188eb35075d81c0fefafb2cc84efa7b
dc.titleA methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networkseng
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/s00521-018-3503-4
dc.type.dcmitext
dc.relation.publisherversionhttp://dx.doi.org/10.1007/s00521-018-3503-4


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