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Palabras clave – provistas por la editorial

multiple cancer types; integrative analysis; omics data; prognosis modeling; classification; gene set enrichment analysis; boosting; kernel method; Bayes factor; Bayesian mixed-effect model; CpG sites; DNA methylation; Ordinal responses; GEE; lipid–environment interaction; longitudinal lipidomics study; penalized variable selection; convolutional neural networks; deep learning; feed-forward neural networks; machine learning; gene regulatory network; nonparanormal graphical model; network substructure; false discovery rate control; gaussian finite mixture model; clustering analysis; uncertainty; expectation-maximization algorithm; classification boundary; gene expression; RNA-seq; n/a