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  "Package": "PrInCE",
  "Title": "Predicting Interactomes from Co-Elution",
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  "BugReports": "https://github.com/fosterlab/PrInCE/issues",
  "Description": "PrInCE (Predicting Interactomes from Co-Elution) uses a\nnaive Bayes classifier trained on dataset-derived features to\nrecover protein-protein interactions from co-elution\nchromatogram profiles. This package contains the R\nimplementation of PrInCE.",
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  "Date/Publication": "2026-04-28 12:49:42 UTC",
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    "fit_curve",
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    "impute_neighbors",
    "is_unweighted",
    "is_weighted",
    "make_feature_from_data_frame",
    "make_feature_from_expression",
    "make_initial_conditions",
    "make_labels",
    "match_matrix_dimensions",
    "predict_ensemble",
    "predict_interactions",
    "PrInCE",
    "replace_missing_data",
    "threshold_precision"
  ],
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      "table": true,
      "tojson": true
    },
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      "table": false,
      "tojson": true
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      "page": "adjacency_matrix_from_data_frame",
      "title": "Create an adjacency matrix from a data frame",
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        "adjacency_matrix_from_data_frame"
      ]
    },
    {
      "page": "adjacency_matrix_from_list",
      "title": "Create an adjacency matrix from a list of complexes",
      "topics": [
        "adjacency_matrix_from_list"
      ]
    },
    {
      "page": "aic",
      "title": "Model selection for Gaussian mixture models",
      "topics": [
        "aic",
        "gaussian_aic",
        "gaussian_aicc",
        "gaussian_bic"
      ]
    },
    {
      "page": "build_gaussians",
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      ]
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      "page": "calculate_autocorrelation",
      "title": "Calculate the autocorrelation for each protein between a pair of co-elution experiments.",
      "topics": [
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      ]
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      "page": "calculate_features",
      "title": "Calculate the default features used to predict interactions in PrInCE",
      "topics": [
        "calculate_features"
      ]
    },
    {
      "page": "calculate_precision",
      "title": "Calculate precision at each point in a sequence",
      "topics": [
        "calculate_precision"
      ]
    },
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      "page": "check_gaussians",
      "title": "Check the format of a list of Gaussians",
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      "title": "Fit a Gaussian mixture model to a co-elution profile",
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      "page": "clean_profile",
      "title": "Preprocess a co-elution profile",
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      ]
    },
    {
      "page": "clean_profiles",
      "title": "Preprocess a co-elution profile matrix",
      "topics": [
        "clean_profiles"
      ]
    },
    {
      "page": "co_apex",
      "title": "Calculate the co-apex score for every protein pair",
      "topics": [
        "co_apex"
      ]
    },
    {
      "page": "concatenate_features",
      "title": "Combine features across multiple replicates",
      "topics": [
        "concatenate_features"
      ]
    },
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      "page": "detect_complexes",
      "title": "Detect significantly interacting complexes in a chromatogram matrix",
      "topics": [
        "detect_complexes"
      ]
    },
    {
      "page": "filter_profiles",
      "title": "Filter a co-elution profile matrix",
      "topics": [
        "filter_profiles"
      ]
    },
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      "page": "fit_curve",
      "title": "Output the fit curve for a given mixture of Gaussians",
      "topics": [
        "fit_curve"
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    },
    {
      "page": "fit_gaussians",
      "title": "Fit a mixture of Gaussians to a chromatogram curve",
      "topics": [
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    },
    {
      "page": "gold_standard",
      "title": "Reference set of human protein complexes",
      "topics": [
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    },
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      "page": "impute_neighbors",
      "title": "Impute single missing values",
      "topics": [
        "impute_neighbors"
      ]
    },
    {
      "page": "is_unweighted",
      "title": "Test whether a network is unweighted",
      "topics": [
        "is_unweighted"
      ]
    },
    {
      "page": "is_weighted",
      "title": "Test whether a network is weighted",
      "topics": [
        "is_weighted"
      ]
    },
    {
      "page": "kristensen",
      "title": "Interactome of HeLa cells",
      "topics": [
        "kristensen"
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    },
    {
      "page": "kristensen_gaussians",
      "title": "Fitted Gaussian mixture models for the 'kristensen' dataset",
      "topics": [
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    },
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      "page": "make_feature_from_data_frame",
      "title": "Create a feature vector for a classifier from a data frame",
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        "make_feature_from_data_frame"
      ]
    },
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      "page": "make_feature_from_expression",
      "title": "Create a feature vector from expression data",
      "topics": [
        "make_feature_from_expression"
      ]
    },
    {
      "page": "make_initial_conditions",
      "title": "Make initial conditions for curve fitting with a mixture of Gaussians",
      "topics": [
        "make_initial_conditions"
      ]
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      "page": "make_labels",
      "title": "Make labels for a classifier based on a gold standard",
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      ]
    },
    {
      "page": "match_matrix_dimensions",
      "title": "Match the dimensions of a query matrix to a profile matrix",
      "topics": [
        "match_matrix_dimensions"
      ]
    },
    {
      "page": "predict_ensemble",
      "title": "Predict interactions using an ensemble of classifiers",
      "topics": [
        "predict_ensemble"
      ]
    },
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      "page": "predict_interactions",
      "title": "Predict interactions given a set of features and examples",
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        "predict_interactions"
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    },
    {
      "page": "PrInCE",
      "title": "PrInCE: Prediction of Interactomes from Co-Elution",
      "topics": [
        "PrInCE"
      ]
    },
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      "page": "replace_missing_data",
      "title": "Replace missing data with median ± random noise",
      "topics": [
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      ]
    },
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      "page": "scott",
      "title": "Cytoplasmic interactome of Jurkat T cells during apoptosis",
      "topics": [
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    },
    {
      "page": "scott_gaussians",
      "title": "Fitted Gaussian mixture models for the 'scott' dataset",
      "topics": [
        "scott_gaussians"
      ]
    },
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      "page": "threshold_precision",
      "title": "Threshold interactions at a given precision cutoff",
      "topics": [
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