dc.contributor.author
Reverter Comes, Ferran
dc.contributor.author
Vegas Lozano, Esteban
dc.contributor.author
Oller i Sala, Josep Maria
dc.date.issued
2020-04-20T13:46:49Z
dc.date.issued
2020-04-20T13:46:49Z
dc.date.issued
2020-04-20T13:46:49Z
dc.identifier
https://hdl.handle.net/2445/156044
dc.description.abstract
Computational methods are needed to combine diverse type of genome-wide data in a meaningful manner. Based on the kernel embedding of conditional probability distributions, a new measure for inferring the degree of association between two multivariate data sources is introduced. We analyze the performance of the proposed measure to integrate mRNA expression, DNA methylation and miRNA expression data.
dc.format
application/pdf
dc.format
application/pdf
dc.publisher
Springer Verlag
dc.relation
Versió postprint del document publicat a: https://doi.org/10.1007/978-3-319-78723-7_43
dc.relation
Lecture Notes in Computer Science, 2018, vol. 10813 LNBI, p. 501-510
dc.relation
https://doi.org/10.1007/978-3-319-78723-7_43
dc.rights
(c) Springer Verlag, 2018
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Articles publicats en revistes (Genètica, Microbiologia i Estadística)
dc.subject
Bioinformàtica
dc.subject
Bioinformatics
dc.title
Kernel conditional Embeddings for associating omic data types
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/acceptedVersion
