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  "Title": "Omics Data Integration Using Kernel Methods",
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  "Authors@R": "c(person(\"Nathalie\", \"Vialaneix\", role = c(\"aut\", \"cre\"), email=\"nathalie.vialaneix@inrae.fr\"),\nperson(\"Celine\", \"Brouard\", role = c(\"aut\"), email=\"celine.brouard@inrae.fr\"),\nperson(\"Remi\", \"Flamary\", role = c(\"aut\"), email=\"remi.flamary@polytechnique.edu\"),\nperson(\"Julien\", \"Henry\", role = c(\"aut\"), email=\"julien.henry@inrae.fr\"),\nperson(\"Jerome\", \"Mariette\", role = c(\"aut\"), email=\"jerome.mariette@inrae.fr\"))",
  "Maintainer": "Nathalie Vialaneix <nathalie.vialaneix@inrae.fr>",
  "Description": "Kernel-based methods are powerful methods for integrating\nheterogeneous types of data. mixKernel aims at providing\nmethods to combine kernel for unsupervised exploratory\nanalysis. Different solutions are provided to compute a\nmeta-kernel, in a consensus way or in a way that best preserves\nthe original topology of the data. mixKernel also integrates\nkernel PCA to visualize similarities between samples in a non\nlinear space and from the multiple source point of view\n<doi:10.1093/bioinformatics/btx682>. A method to select (as\nwell as funtions to display) important variables is also\nprovided <doi:10.1093/nargab/lqac014>.",
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