A methodology for the detection of relevant single nucleotide polymorphism in prostate cancer by means of multivariate adaptive regression splines and backpropagation artificial neural networks

dc.contributor.author	Sánchez Lasheras, Juan Enrique
dc.contributor.author	Tardón García, Adonina
dc.contributor.author	Fernández Tardón, Guillermo
dc.contributor.author	Suárez Gómez, Sergio Luis
dc.contributor.author	Martín Sánchez, V.
dc.contributor.author	González Donquiles, Carmen
dc.contributor.author	Cos Juez, Francisco Javier de
dc.date.accessioned	2018-02-06T10:11:18Z
dc.date.available	2018-02-06T10:11:18Z
dc.date.issued	2018
dc.identifier.isbn	978-3-319-67179-6
dc.identifier.issn	2194-5357
dc.identifier.uri	http://hdl.handle.net/10651/45476
dc.description	SOCO 2017, ICEUTE 2017, CISIS 2017 (León. 2017)
dc.format.extent	p. 391-399
dc.language.iso	eng
dc.relation.ispartof	International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing, 649
dc.rights	©,
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028655681&doi=10.1007%2f978-3-319-67180-2_38&partnerID=40&md5=b5ae31cd28d4ce090961b4a51ba1dab3
dc.title	A methodology for the detection of relevant single nucleotide polymorphism in prostate cancer by means of multivariate adaptive regression splines and backpropagation artificial neural networks	eng
dc.type	conference output	spa
dc.identifier.doi	10.1007/978-3-319-67180-2_38
dc.relation.publisherversion	http://dx.doi.org/10.1007/978-3-319-67180-2_38

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