{
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  "Type": "Package",
  "Package": "ceRNAnetsim",
  "Title": "Regulation Simulator of Interaction between miRNA and Competing\nRNAs (ceRNA)",
  "Version": "1.25.0",
  "Authors@R": "c(person(given = \"Selcen\",\nfamily = \"Ari Yuka\",\nrole = c(\"aut\", \"cre\"),\nemail = \"selcenarii@gmail.com\",\ncomment = c(ORCID = \"0000-0002-0028-2453\")),\nperson(given = \"Alper\",\nfamily = \"Yilmaz\",\nrole = \"aut\",\nemail = \"alperyilmaz@gmail.com\",\ncomment = c(ORCID = \"0000-0002-8827-4887\")))",
  "Description": "This package simulates regulations of ceRNA (Competing\nEndogenous) expression levels after a expression level change\nin one or more miRNA/mRNAs. The methodolgy adopted by the\npackage has potential to incorparate any ceRNA (circRNA,\nlincRNA, etc.) into miRNA:target interaction network.  The\npackage basically distributes miRNA expression over available\nceRNAs where each ceRNA attracks miRNAs proportional to its\namount. But, the package can utilize multiple parameters that\nmodify miRNA effect on its target (seed type, binding energy,\nbinding location, etc.).  The functions handle the given\ndataset as graph object and the processes progress via edge and\nnode variables.",
  "License": "GPL (>= 3.0)",
  "URL": "https://github.com/selcenari/ceRNAnetsim",
  "BugReports": "https://github.com/selcenari/ceRNAnetsim/issues",
  "VignetteBuilder": "knitr",
  "biocViews": "NetworkInference, SystemsBiology, Network, GraphAndNetwork,\nTranscriptomics",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:51:27 UTC",
  "RemoteUrl": "https://github.com/bioc/ceRNAnetsim",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-29 08:25:30 UTC",
    "User": "root"
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  "Author": "Selcen Ari Yuka [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-0028-2453>),\nAlper Yilmaz [aut] (ORCID: <https://orcid.org/0000-0002-8827-4887>)",
  "Maintainer": "Selcen Ari Yuka <selcenarii@gmail.com>",
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  "_created": "2026-05-29T08:25:30.000Z",
  "_published": "2026-05-29T10:09:15.083Z",
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    "committer": "A Wokaty <andres.wokaty@sph.cuny.edu>",
    "message": "bump x.y.z version to odd y following creation of RELEASE_3_23 branch\n",
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    "systemsbiology",
    "network",
    "graphandnetwork",
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    "network-biology",
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    "description": "Software for the analysis and comprehension of high-throughput genomic data"
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    "source": "https://www.bioconductor.org/packages/stats/bioc/ceRNAnetsim"
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  "_exports": [
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    "find_affected_nodes",
    "find_iteration",
    "find_node_perturbation",
    "find_targeting_nodes",
    "priming_graph",
    "simulate",
    "simulate_vis",
    "update_how",
    "update_nodes",
    "update_variables",
    "vis_graph"
  ],
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      "name": "huge_example",
      "title": "huge example",
      "object": "huge_example",
      "file": "huge_example.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "competing",
        "miRNA",
        "competing_counts",
        "mirnaexpression_normal",
        "Energy",
        "region_effect",
        "seed_type_effect"
      ],
      "rows": 26176,
      "table": true,
      "tojson": true
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        "miRNAs",
        "Gene_expression",
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        "seeds",
        "targeting_region",
        "Energy"
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      "table": true,
      "tojson": true
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      "title": "midsamp_new_counts",
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        "miRNA",
        "Competing_count",
        "miRNA_count"
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      "table": true,
      "tojson": true
    },
    {
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      "title": "minsamp",
      "object": "minsamp",
      "file": "minsamp.rda",
      "class": [
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      ],
      "fields": [
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        "miRNA",
        "Competing_expression",
        "miRNA_expression",
        "seed_type",
        "region",
        "energy"
      ],
      "rows": 7,
      "table": true,
      "tojson": true
    },
    {
      "name": "mirtarbasegene",
      "title": "mirtarbasegene",
      "object": "mirtarbasegene",
      "file": "mirtarbasegene.rda",
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        "data.frame"
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      "fields": [
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        "Target"
      ],
      "rows": 380627,
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      "file": "new_counts.rda",
      "class": [
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      ],
      "fields": [
        "Competing",
        "miRNA",
        "Competing_count",
        "miRNA_count"
      ],
      "rows": 7,
      "table": true,
      "tojson": true
    },
    {
      "name": "TCGA_E9_A1N5_mirnanormal",
      "title": "TCGA_E9_A1N5_mirnanormal",
      "object": "TCGA_E9_A1N5_mirnanormal",
      "file": "TCGA_E9_A1N5_mirnanormal.rda",
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        "data.frame"
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        "mirbase_ID",
        "miRNA",
        "Precusor",
        "total_read",
        "total_RPM"
      ],
      "rows": 750,
      "table": true,
      "tojson": true
    },
    {
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      "title": "TCGA_E9_A1N5_mirnatumor",
      "object": "TCGA_E9_A1N5_mirnatumor",
      "file": "TCGA_E9_A1N5_mirnatumor.rda",
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        "tbl",
        "data.frame"
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        "mirbase_ID",
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        "Precusor",
        "total_read",
        "total_RPM"
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      "rows": 648,
      "table": true,
      "tojson": true
    },
    {
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      "title": "TCGA_E9_A1N5_normal",
      "object": "TCGA_E9_A1N5_normal",
      "file": "TCGA_E9_A1N5_normal.rda",
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        "gene_expression"
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      "object": "TCGA_E9_A1N5_tumor",
      "file": "TCGA_E9_A1N5_tumor.rda",
      "class": [
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        "gene_expression"
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    {
      "page": "calc_perturbation",
      "title": "Calculates average expression changes of all nodes except trigger and finds the perturbed node count for a given node.",
      "topics": [
        "calc_perturbation"
      ]
    },
    {
      "page": "find_affected_nodes",
      "title": "Finds top affected nodes for perturbation from a particular node",
      "topics": [
        "find_affected_nodes"
      ]
    },
    {
      "page": "find_iteration",
      "title": "Finds the iteration which provides maximum affected node number",
      "topics": [
        "find_iteration"
      ]
    },
    {
      "page": "find_node_perturbation",
      "title": "Calculates average expression changes of all (or specified) nodes except trigger and finds the perturbed node count for all (or specified) nodes in system.",
      "topics": [
        "find_node_perturbation"
      ]
    },
    {
      "page": "find_targeting_nodes",
      "title": "Finds potential affecting node for given particular target.",
      "topics": [
        "find_targeting_nodes"
      ]
    },
    {
      "page": "huge_example",
      "title": "huge example",
      "topics": [
        "huge_example"
      ]
    },
    {
      "page": "midsamp",
      "title": "midsamp",
      "topics": [
        "midsamp"
      ]
    },
    {
      "page": "midsamp_new_counts",
      "title": "midsamp_new_counts",
      "topics": [
        "midsamp_new_counts"
      ]
    },
    {
      "page": "minsamp",
      "title": "minsamp",
      "topics": [
        "minsamp"
      ]
    },
    {
      "page": "mirtarbasegene",
      "title": "mirtarbasegene",
      "topics": [
        "mirtarbasegene"
      ]
    },
    {
      "page": "new_counts",
      "title": "new_counts",
      "topics": [
        "new_counts"
      ]
    },
    {
      "page": "priming_graph",
      "title": "Converts the given dataframe using first variable as competing and the second as miRNA. The function converts the given dataframe using first variable as competing and the second as miRNA. If user defines interaction factors as affinity or degradation, the factors are taken into account.",
      "topics": [
        "priming_graph"
      ]
    },
    {
      "page": "simulate",
      "title": "Utilizes the change in expression value/s as triggering.",
      "topics": [
        "simulate"
      ]
    },
    {
      "page": "simulate_vis",
      "title": "Provides visualisation of the graph in addition to simulate function.",
      "topics": [
        "simulate_vis"
      ]
    },
    {
      "page": "TCGA_E9_A1N5_mirnanormal",
      "title": "TCGA_E9_A1N5_mirnanormal",
      "topics": [
        "TCGA_E9_A1N5_mirnanormal"
      ]
    },
    {
      "page": "TCGA_E9_A1N5_mirnatumor",
      "title": "TCGA_E9_A1N5_mirnatumor",
      "topics": [
        "TCGA_E9_A1N5_mirnatumor"
      ]
    },
    {
      "page": "TCGA_E9_A1N5_normal",
      "title": "TCGA_E9_A1N5_normal",
      "topics": [
        "TCGA_E9_A1N5_normal"
      ]
    },
    {
      "page": "TCGA_E9_A1N5_tumor",
      "title": "TCGA_E9_A1N5_tumor",
      "topics": [
        "TCGA_E9_A1N5_tumor"
      ]
    },
    {
      "page": "update_how",
      "title": "Converts the count value of the given node.",
      "topics": [
        "update_how"
      ]
    },
    {
      "page": "update_nodes",
      "title": "Carries variables from edge to node.",
      "topics": [
        "update_nodes"
      ]
    },
    {
      "page": "update_variables",
      "title": "Replaces new values with previous values of competing or miRNA counts.",
      "topics": [
        "update_variables"
      ]
    },
    {
      "page": "vis_graph",
      "title": "Provides visualisation of the graph.",
      "topics": [
        "vis_graph"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/ceRNAnetsim/raw/HEAD/README.md",
  "_rundeps": [
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  "_vignettes": [
    {
      "source": "convenient_iteration.Rmd",
      "filename": "convenient_iteration.html",
      "title": "Calculating Number of Iterations Required to Reach Steady-State",
      "author": "Selcen Ari",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Introduction",
        "2. Installation",
        "3. Comparison of gaining steady-state durations of middle and minimal datasets",
        "3.1. Suggestion for simulation iteration",
        "3.2. Find appropriate iteration number with find_iteration and then simulate accordingly",
        "4. What is perturbation efficiency?",
        "4.1. How does the calc_perturbation() work?",
        "4.2. A Short-cut: Finding perturbation efficiencies for whole nodes of network",
        "5. Session Info"
      ],
      "created": "2019-03-15 09:49:24",
      "modified": "2019-11-25 09:36:05",
      "commits": 34
    },
    {
      "source": "mirtarbase_example.Rmd",
      "filename": "mirtarbase_example.html",
      "title": "A TCGA dataset application",
      "author": "Selcen Ari",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Introduction",
        "2. Installation",
        "3. Integration of dataset which includes only miRNA and gene expression values",
        "3.1. miRNA:target pairs",
        "3.2. Gene expression in normal and tumor samples",
        "3.3. miRNA expression data",
        "3.4. Integrating and analysing data",
        "3.5. The sum of two conditions:",
        "4. Dataset (huge_example) which includes miRNA and gene expressions and miRNA:target interaction factors",
        "4.1. Description of the huge_example dataset",
        "4.2. Select a node as trigger",
        "5. Finding perturbation efficiency on an experimental dataset",
        "6. Session Info",
        "References"
      ],
      "created": "2019-03-18 14:14:48",
      "modified": "2019-11-26 19:10:25",
      "commits": 45
    },
    {
      "source": "auxiliary_commands.Rmd",
      "filename": "auxiliary_commands.html",
      "title": "The auxiliary commands which can help to the users",
      "author": "Selcen Ari",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Introduction",
        "2. Installation",
        "3. Selection of perturbing element from dataset",
        "3.1. Selection of HIST1H3H gene at vignette How does the system behave in mirtarbase dataset without interaction factors?",
        "3.2. Selection of ACTB gene at vignette How does the system behave in mirtarbase dataset without interaction factors?",
        "4. Determination of ACTB gene perturbation efficiency with different expression level changes",
        "5. Session Info"
      ],
      "created": "2019-08-06 13:53:43",
      "modified": "2019-11-26 12:01:55",
      "commits": 8
    },
    {
      "source": "basic_usage.Rmd",
      "filename": "basic_usage.html",
      "title": "Basic Use of ceRNAnetsim",
      "author": "Selcen Ari",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Introduction",
        "2. Installation",
        "3. About the data",
        "4. Simulation via expression values of miRNAs and genes in minsamp dataset",
        "4.1. Handle basic dataset",
        "4.2. Trigger a change",
        "4.3. Simulate the changes in graph",
        "4.4. A special case: knockdown",
        "5. Simulation via interaction factors in addition to expression values of miRNAs and genes in minsamp dataset",
        "5.1. Change expression level of one or more nodes in the graph",
        "5.2. Update the node variables with edge variables.",
        "5.3. Simulate the model",
        "5.4. Visualisation of the graph",
        "6. Session Info"
      ],
      "created": "2019-08-06 07:24:41",
      "modified": "2019-11-24 19:21:53",
      "commits": 16
    }
  ],
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  "_indexed": true,
  "_nocasepkg": "cernanetsim",
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