{
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  "Package": "driveR",
  "Title": "Prioritizing Cancer Driver Genes Using Genomics Data",
  "Version": "0.5.0.9000",
  "Authors@R": "person(given = \"Ege\",\nfamily = \"Ulgen\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"egeulgen@gmail.com\",\ncomment = c(ORCID = \"0000-0003-2090-3621\"))",
  "Maintainer": "Ege Ulgen <egeulgen@gmail.com>",
  "Description": "Cancer genomes contain large numbers of somatic\nalterations but few genes drive tumor development. Identifying\ncancer driver genes is critical for precision oncology. Most of\ncurrent approaches either identify driver genes based on\nmutational recurrence or using estimated scores predicting the\nfunctional consequences of mutations. 'driveR' is a tool for\npersonalized or batch analysis of genomic data for driver gene\nprioritization by combining genomic information and prior\nbiological knowledge. As features, 'driveR' uses coding impact\nmetaprediction scores, non-coding impact scores, somatic copy\nnumber alteration scores, hotspot gene/double-hit gene\ncondition, 'phenolyzer' gene scores and memberships to\ncancer-related KEGG pathways. It uses these features to\nestimate cancer-type-specific probability for each gene of\nbeing a cancer driver using the related task of a multi-task\nlearning classification model. The method is described in\ndetail in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel\nmethod for prioritizing cancer driver genes using somatic\ngenomics data. BMC Bioinformatics\n<doi:10.1186/s12859-021-04203-7>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "RoxygenNote": "7.3.3",
  "URL": "https://egeulgen.github.io/driveR/,\nhttps://github.com/egeulgen/driveR",
  "BugReports": "https://github.com/egeulgen/driveR/issues",
  "biocViews": "",
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  "Repository": "https://egeulgen.r-universe.dev",
  "Date/Publication": "2025-12-22 11:39:41 UTC",
  "RemoteUrl": "https://github.com/egeulgen/driver",
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  "Author": "Ege Ulgen [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0003-2090-3621>)",
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  "_topics": [
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    "prioritize_driver_genes"
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      "title": "Example Cohort-level Features Table for Driver Prioritization",
      "object": "example_cohort_features_table",
      "class": [
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      ],
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      "table": true,
      "tojson": true
    },
    {
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      "title": "Example Cohort-level Somatic Copy Number Alteration Table",
      "object": "example_cohort_scna_table",
      "class": [
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      ],
      "fields": [
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        "start",
        "end",
        "log2ratio",
        "tumor_id"
      ],
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      "table": true,
      "tojson": true
    },
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      "title": "Example Features Table for Driver Prioritization",
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      ],
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        "metaprediction_score",
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    },
    {
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      "title": "Example Gene-level Somatic Copy Number Alteration Table",
      "object": "example_gene_scna_table",
      "class": [
        "data.frame"
      ],
      "fields": [
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        "log2ratio"
      ],
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      "table": true,
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        "description"
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      "table": true,
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        "description"
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      "table": true,
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      "table": false,
      "tojson": true
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      "page": "create_features_df",
      "title": "Create Data Frame of Features for Driver Gene Prioritization",
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    {
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      "topics": [
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      "topics": [
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      "page": "determine_hotspot_genes",
      "title": "Determine Hotspot Containing Genes",
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      "page": "driveR",
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      "page": "example_cohort_scna_table",
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      "topics": [
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      "page": "predict_coding_impact",
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      "title": "Tumor type specific probability thresholds",
      "topics": [
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