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

dc.contributor.author	Sánchez Lasheras, Juan Enrique
dc.contributor.author	González Donquiles, Carmen
dc.contributor.author	García Nieto, Paulino José
dc.contributor.author	Jiménez Moleón, José Juan
dc.contributor.author	Salas, Dolores
dc.contributor.author	Suárez Gómez, Sergio Luis
dc.contributor.author	Molina de la Torre, Antonio José
dc.contributor.author	González-Nuevo González, Joaquín
dc.contributor.author	Bonavera, Laura
dc.contributor.author	Carballido Landeira, Jorge
dc.contributor.author	Cos Juez, Francisco Javier de
dc.date.accessioned	2018-10-08T10:23:36Z
dc.date.available	2018-10-08T10:23:36Z
dc.date.issued	2018
dc.identifier.citation	Neural Computing and Applications, p. 1-8 (2018); doi:10.1007/s00521-018-3503-4
dc.identifier.issn	0941-0643
dc.identifier.uri	http://hdl.handle.net/10651/48932
dc.format.extent	p. 1-8
dc.language.iso	eng
dc.relation.ispartof	Neural Computing and Applications
dc.rights	© The Natural Computing Applications Forum 2018
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046492371&doi=10.1007%2fs00521-018-3503-4&partnerID=40&md5=d188eb35075d81c0fefafb2cc84efa7b
dc.title	A methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networks	eng
dc.type	journal article
dc.identifier.doi	10.1007/s00521-018-3503-4
dc.relation.publisherversion	http://dx.doi.org/10.1007/s00521-018-3503-4

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