{ "_id": "6a1088e4acfb0bcc41cb250e", "Package": "kgraph", "Title": "Knowledge Graphs Constructions and Visualizations", "Version": "1.2.0", "Authors@R": "c(person(\"Thomas\", \"Charlon\", role = c(\"aut\", \"cre\"),\nemail = \"charlon@protonmail.com\",\ncomment = c(ORCID = \"0000-0001-7497-0470\")),\nperson(\"Hongyi\", \"Yuan\", role = \"ctb\", comment = c(ORCID = \"0000-0003-2597-1973\")),\nperson(\"CELEHS\", role = \"aut\", comment = \"\"),\nperson(\"PARSE Health\", role = \"aut\", comment = \"\"))", "Description": "Knowledge graphs enable to efficiently visualize and gain\ninsights into large-scale data analysis results, as p-values\nfrom multiple studies or embedding data matrices. The usual\nworkflow is a user providing a data frame of association\nstudies results and specifying target nodes, e.g. phenotypes,\nto visualize. The knowledge graph then shows all the features\nwhich are significantly associated with the phenotype, with the\nedges being proportional to the association scores. As the user\nadds several target nodes and grouping information about the\nnodes such as biological pathways, the construction of such\ngraphs soon becomes complex. The 'kgraph' package aims to\nenable users to easily build such knowledge graphs, and\nprovides two main features: first, to enable building a\nknowledge graph based on a data frame of concepts\nrelationships, be it p-values or cosine similarities; second,\nto enable determining an appropriate cut-off on cosine\nsimilarities from a complete embedding matrix, to enable the\nbuilding of a knowledge graph directly from an embedding\nmatrix. The 'kgraph' package provides several display, layout\nand cut-off options, and has already proven useful to\nresearchers to enable them to visualize large sets of p-value\nassociations with various phenotypes, and to quickly be able to\nvisualize embedding results. Two example datasets are provided\nto demonstrate these behaviors, and several live 'shiny'\napplications are hosted by the CELEHS laboratory and Parse\nHealth, as the KESER Mental Health application\n based on Hong\nC. (2021) .", "VignetteBuilder": "knitr", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "URL": "https://gitlab.com/thomaschln/kgraph", "BugReports": "https://gitlab.com/thomaschln/kgraph/-/issues", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libuv1-dev\nlibxml2-dev zlib1g-dev", "Repository": "https://thomaschln.r-universe.dev", "Date/Publication": "2025-04-04 19:00:52 UTC", "RemoteUrl": "https://gitlab.com/thomaschln/kgraph", "RemoteRef": "HEAD", "RemoteSha": "90cf6f20bb782928e099db774ce96b283b50d6b1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-05 07:23:02 UTC", "User": "root" }, "Author": "Thomas Charlon [aut, cre] (ORCID:\n),\nHongyi Yuan [ctb] (ORCID: ),\nCELEHS [aut] (),\nPARSE Health [aut] ()", "Maintainer": "Thomas Charlon ", "MD5sum": "591d6aa9234b940a4620c67a4c7342f3", "_user": "thomaschln", "_type": "src", "_file": "kgraph_1.2.0.tar.gz", "_fileid": "f19f025a961e24734ec7ace5569c0533e332053a23afb520cee0918fd736e6ff", "_filesize": 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