A new algorithm for multivariate genome wide association studies based on differential evolution and extreme learning machines

dc.contributor.author	Álvarez Gutiérrez, David
dc.contributor.author	Sánchez Lasheras, Fernando
dc.contributor.author	Martín Sánchez, V.
dc.contributor.author	Suárez Gómez, Sergio Luis
dc.contributor.author	Moreno, V.
dc.contributor.author	Moratalla Navarro, F.
dc.contributor.author	Molina de la Torre, A. J.
dc.date.accessioned	2022-11-08T12:42:29Z
dc.date.available	2022-11-08T12:42:29Z
dc.date.issued	2022
dc.identifier.citation	Mathematics, 10(7) (2022); doi:10.3390/math10071024
dc.identifier.issn	2227-7390
dc.identifier.uri	http://hdl.handle.net/10651/65324
dc.description.sponsorship	Agency for Management of University and Research Grants (AGAUR) of the Catalan Government [2017SGR723]; Instituto de Salud Carlos III; FEDER funds-a way to build Europe; Spanish Association Against Cancer (AECC) Scientific Foundation grant [GCTRA18022MORE]
dc.language.iso	eng
dc.relation.ispartof	Mathematics
dc.rights	© 2022 Los auotores
dc.rights	CC Reconocimiento 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127603671&doi=10.3390%2fmath10071024&partnerID=40&md5=5ba0f04f3c93b91d8ed54d68a8ead3df
dc.title	A new algorithm for multivariate genome wide association studies based on differential evolution and extreme learning machines
dc.type	journal article
dc.identifier.doi	10.3390/math10071024
dc.relation.publisherversion	http://dx.doi.org/10.3390/math10071024
dc.rights.accessRights	open access
dc.type.hasVersion	VoR

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