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  "Package": "networkABC",
  "Type": "Package",
  "Title": "Network Reverse Engineering with Approximate Bayesian\nComputation",
  "Version": "0.9-1",
  "Date": "2025-09-09",
  "Authors@R": "c(\nperson(given = \"Frederic\", family= \"Bertrand\", role = c(\"cre\", \"aut\"), email = \"frederic.bertrand@lecnam.net\", comment = c(ORCID = \"0000-0002-0837-8281\")),\nperson(given = \"Myriam\", family= \"Maumy-Bertrand\", role = c(\"aut\"), email = \"myriam.maumy@ehesp.fr\", comment = c(ORCID = \"0000-0002-4615-1512\")),\nperson(given = \"Khadija\", family= \"Musayeva\", role = c(\"ctb\"), email = \"khadija@musayeva.net\"),\nperson(given = \"Nicolas\", family= \"Jung\", role = c(\"ctb\"), email = \"nicolas.jung@unistra.fr\"),\nperson(family = \"Université de Strasbourg\", role = c(\"cph\")),\nperson(family = \"CNRS\", role = c(\"cph\")))",
  "Author": "Frederic Bertrand [cre, aut]\n(<https://orcid.org/0000-0002-0837-8281>), Myriam\nMaumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>),\nKhadija Musayeva [ctb], Nicolas Jung [ctb], Université de\nStrasbourg [cph], CNRS [cph]",
  "Maintainer": "Frederic Bertrand <frederic.bertrand@lecnam.net>",
  "Description": "We developed an inference tool based on approximate\nBayesian computation to decipher network data and assess the\nstrength of the inferred links between network's actors. It is\na new multi-level approximate Bayesian computation (ABC)\napproach. At the first level, the method captures the global\nproperties of the network, such as a scale-free structure and\nclustering coefficients, whereas the second level is targeted\nto capture local properties, including the probability of each\ncouple of genes being linked. Up to now, Approximate Bayesian\nComputation (ABC) algorithms have been scarcely used in that\nsetting and, due to the computational overhead, their\napplication was limited to a small number of genes. On the\ncontrary, our algorithm was made to cope with that issue and\nhas low computational cost. It can be used, for instance, for\nelucidating gene regulatory network, which is an important step\ntowards understanding the normal cell physiology and complex\npathological phenotype. Reverse-engineering consists in using\ngene expressions over time or over different experimental\nconditions to discover the structure of the gene network in a\ntargeted cellular process. The fact that gene expression data\nare usually noisy, highly correlated, and have high\ndimensionality explains the need for specific statistical\nmethods to reverse engineer the underlying network.",
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  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "BugReports": "https://github.com/fbertran/networkABC/issues/",
  "RoxygenNote": "7.3.3",
  "Config/testthat/edition": "3",
  "Repository": "https://fbertran.r-universe.dev",
  "Date/Publication": "2025-09-19 21:28:49 UTC",
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    "User": "root"
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    "author": "Frederic Bertrand <frederic.bertrand@utt.fr>",
    "committer": "Frederic Bertrand <frederic.bertrand@utt.fr>",
    "message": "* Maintainer email updated\n* Added package doi\n",
    "time": 1758317329
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    "login": "fbertran",
    "orcid": "0000-0002-0837-8281",
    "twitter": "@BertrandFrdric2",
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  "_realowner": "fbertran",
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