# name
Kernel conditional Embeddings for associating omic data types

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.ispartof[].value
Lecture Notes in Computer Science, 2018, vol. 10813  LNBI, p. 501-510

# metadata.dc.source[].value
Articles publicats en revistes (Genètica, Microbiologia i Estadística)

# metadata.dc.title[].value
Kernel conditional Embeddings for associating omic data types