{
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  "Package": "Patterns",
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  "Title": "Deciphering Biological Networks with Patterned Heterogeneous\nMeasurements",
  "Version": "1.7",
  "Date": "2025-09-09",
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  "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\")))",
  "Author": "Frederic Bertrand [cre, aut]\n(<https://orcid.org/0000-0002-0837-8281>), Myriam\nMaumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>)",
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  "Description": "A modeling tool dedicated to biological network modeling\n(Bertrand and others 2020,\n<doi:10.1093/bioinformatics/btaa855>). It allows for single or\njoint modeling of, for instance, genes and proteins. It starts\nwith the selection of the actors that will be the used in the\nreverse engineering upcoming step. An actor can be included in\nthat selection based on its differential measurement (for\ninstance gene expression or protein abundance) or on its time\ncourse profile. Wrappers for actors clustering functions and\ncluster analysis are provided. It also allows reverse\nengineering of biological networks taking into account the\nobserved time course patterns of the actors. Many inference\nfunctions are provided and dedicated to get specific features\nfor the inferred network such as sparsity, robust links, high\nconfidence links or stable through resampling links. Some\nsimulation and prediction tools are also available for cascade\nnetworks (Jung and others 2014,\n<doi:10.1093/bioinformatics/btt705>). Example of use with\nmicroarray or RNA-Seq data are provided.",
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