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  "Package": "LBDiscover",
  "Title": "Literature-Based Discovery Tools for Biomedical Research",
  "Version": "0.1.0",
  "Date": "2025-05-14",
  "Authors@R": "person(\"Chao Liu\", email = \"chaoliu@cedarville.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-9979-8272\"))",
  "Description": "A suite of tools for literature-based discovery in\nbiomedical research. Provides functions for retrieving\nscientific articles from PubMed and other NCBI databases,\nextracting biomedical entities (diseases, drugs, genes, etc.),\nbuilding co-occurrence networks, and applying various discovery\nmodels including ABC, AnC, LSI, and BITOLA. The package also\nincludes visualization tools for exploring discovered\nconnections.",
  "License": "GPL-3",
  "URL": "https://github.com/chaoliu-cl/LBDiscover,\nhttp://liu-chao.site/LBDiscover/,\nhttps://liu-chao.site/LBDiscover/",
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  "Repository": "https://chaoliu-cl.r-universe.dev",
  "Date/Publication": "2025-10-05 00:54:04 UTC",
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  "Author": "Chao Liu [aut, cre] (ORCID: <https://orcid.org/0000-0002-9979-8272>)",
  "Maintainer": "Chao Liu <chaoliu@cedarville.edu>",
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    "load_results",
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    "segment_sentences",
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    "validate_umls_key",
    "vec_preprocess",
    "vis_abc_heatmap",
    "vis_abc_network",
    "vis_heatmap",
    "vis_network"
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      "page": "abc_model",
      "title": "Apply the ABC model for literature-based discovery with improved filtering",
      "topics": [
        "abc_model"
      ]
    },
    {
      "page": "abc_model_opt",
      "title": "Optimize ABC model calculations for large matrices",
      "topics": [
        "abc_model_opt"
      ]
    },
    {
      "page": "abc_model_sig",
      "title": "Apply the ABC model with statistical significance testing",
      "topics": [
        "abc_model_sig"
      ]
    },
    {
      "page": "abc_timeslice",
      "title": "Apply time-sliced ABC model for validation",
      "topics": [
        "abc_timeslice"
      ]
    },
    {
      "page": "anc_model",
      "title": "ANC model for literature-based discovery with biomedical term filtering",
      "topics": [
        "anc_model"
      ]
    },
    {
      "page": "bitola_model",
      "title": "Apply BITOLA-style discovery model",
      "topics": [
        "bitola_model"
      ]
    },
    {
      "page": "calc_bibliometrics",
      "title": "Calculate basic bibliometric statistics",
      "topics": [
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      ]
    },
    {
      "page": "calc_doc_sim",
      "title": "Calculate document similarity using TF-IDF and cosine similarity",
      "topics": [
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      ]
    },
    {
      "page": "clear_pubmed_cache",
      "title": "Clear PubMed cache",
      "topics": [
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      ]
    },
    {
      "page": "cluster_docs",
      "title": "Cluster documents using K-means",
      "topics": [
        "cluster_docs"
      ]
    },
    {
      "page": "compare_terms",
      "title": "Compare term frequencies between two corpora",
      "topics": [
        "compare_terms"
      ]
    },
    {
      "page": "create_citation_net",
      "title": "Create a citation network from article data",
      "topics": [
        "create_citation_net"
      ]
    },
    {
      "page": "create_comat",
      "title": "Create co-occurrence matrix without explicit entity type constraints",
      "topics": [
        "create_comat"
      ]
    },
    {
      "page": "create_report",
      "title": "Generate a comprehensive discovery report",
      "topics": [
        "create_report"
      ]
    },
    {
      "page": "create_sparse_comat",
      "title": "Create a sparse co-occurrence matrix",
      "topics": [
        "create_sparse_comat"
      ]
    },
    {
      "page": "create_tdm",
      "title": "Create a term-document matrix from preprocessed text",
      "topics": [
        "create_tdm"
      ]
    },
    {
      "page": "create_term_document_matrix",
      "title": "Create a term-document matrix from preprocessed text",
      "topics": [
        "create_term_document_matrix"
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    },
    {
      "page": "detect_lang",
      "title": "Detect language of text",
      "topics": [
        "detect_lang"
      ]
    },
    {
      "page": "diversify_abc",
      "title": "Enforce diversity in ABC model results",
      "topics": [
        "diversify_abc"
      ]
    },
    {
      "page": "enhance_abc_kb",
      "title": "Enhance ABC results with external knowledge",
      "topics": [
        "enhance_abc_kb"
      ]
    },
    {
      "page": "eval_evidence",
      "title": "Evaluate literature support for discovery results",
      "topics": [
        "eval_evidence"
      ]
    },
    {
      "page": "export_chord",
      "title": "Export interactive HTML chord diagram for ABC connections",
      "topics": [
        "export_chord"
      ]
    },
    {
      "page": "export_chord_diagram",
      "title": "Export interactive HTML chord diagram for ABC connections",
      "topics": [
        "export_chord_diagram"
      ]
    },
    {
      "page": "export_network",
      "title": "Export ABC results to simple HTML network",
      "topics": [
        "export_network"
      ]
    },
    {
      "page": "extract_entities",
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      "topics": [
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      ]
    },
    {
      "page": "extract_entities_workflow",
      "title": "Extract entities from text with improved efficiency using only base R",
      "topics": [
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      ]
    },
    {
      "page": "extract_ner",
      "title": "Perform named entity recognition on text",
      "topics": [
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      ]
    },
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      "title": "Extract n-grams from text",
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      ]
    },
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      "title": "Extract common terms from a corpus",
      "topics": [
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      ]
    },
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      "title": "Apply topic modeling to a corpus",
      "topics": [
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    },
    {
      "page": "filter_by_type",
      "title": "Filter a co-occurrence matrix by entity type",
      "topics": [
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      ]
    },
    {
      "page": "find_abc_all",
      "title": "Find all potential ABC connections",
      "topics": [
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      ]
    },
    {
      "page": "find_similar_docs",
      "title": "Find similar documents for a given document",
      "topics": [
        "find_similar_docs"
      ]
    },
    {
      "page": "find_term",
      "title": "Find primary term in co-occurrence matrix",
      "topics": [
        "find_term"
      ]
    },
    {
      "page": "gen_report",
      "title": "Generate comprehensive discovery report",
      "topics": [
        "gen_report"
      ]
    },
    {
      "page": "get_dict_cache",
      "title": "Get dictionary cache environment",
      "topics": [
        "get_dict_cache"
      ]
    },
    {
      "page": "get_pmc_fulltext",
      "title": "Retrieve full text from PubMed Central",
      "topics": [
        "get_pmc_fulltext"
      ]
    },
    {
      "page": "get_term_vars",
      "title": "Extract term variations from text corpus",
      "topics": [
        "get_term_vars"
      ]
    },
    {
      "page": "get_type_dist",
      "title": "Get entity type distribution from co-occurrence matrix",
      "topics": [
        "get_type_dist"
      ]
    },
    {
      "page": "is_valid_biomedical_entity",
      "title": "Determine if a term is likely a specific biomedical entity with improved accuracy",
      "topics": [
        "is_valid_biomedical_entity"
      ]
    },
    {
      "page": "load_dictionary",
      "title": "Load biomedical dictionaries with improved error handling",
      "topics": [
        "load_dictionary"
      ]
    },
    {
      "page": "load_results",
      "title": "Load saved results from a file",
      "topics": [
        "load_results"
      ]
    },
    {
      "page": "lsi_model",
      "title": "LSI model with enhanced biomedical term filtering and NLP verification",
      "topics": [
        "lsi_model"
      ]
    },
    {
      "page": "map_ontology",
      "title": "Map terms to biomedical ontologies",
      "topics": [
        "map_ontology"
      ]
    },
    {
      "page": "merge_entities",
      "title": "Combine and deduplicate entity datasets",
      "topics": [
        "merge_entities"
      ]
    },
    {
      "page": "merge_results",
      "title": "Merge multiple search results",
      "topics": [
        "merge_results"
      ]
    },
    {
      "page": "min_results",
      "title": "Ensure minimum results for visualization",
      "topics": [
        "min_results"
      ]
    },
    {
      "page": "ncbi_search",
      "title": "Search NCBI databases for articles or data",
      "topics": [
        "ncbi_search"
      ]
    },
    {
      "page": "parallel_analysis",
      "title": "Apply parallel processing for document analysis",
      "topics": [
        "parallel_analysis"
      ]
    },
    {
      "page": "perm_test_abc",
      "title": "Perform randomization test for ABC model",
      "topics": [
        "perm_test_abc"
      ]
    },
    {
      "page": "plot_heatmap",
      "title": "Create heatmap visualization from results",
      "topics": [
        "plot_heatmap"
      ]
    },
    {
      "page": "plot_network",
      "title": "Create network visualization from results",
      "topics": [
        "plot_network"
      ]
    },
    {
      "page": "prep_articles",
      "title": "Prepare articles for report generation",
      "topics": [
        "prep_articles"
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    },
    {
      "page": "preprocess_text",
      "title": "Preprocess article text",
      "topics": [
        "preprocess_text"
      ]
    },
    {
      "page": "pubmed_search",
      "title": "Search PubMed for articles with optimized performance",
      "topics": [
        "pubmed_search"
      ]
    },
    {
      "page": "query_external_api",
      "title": "Query external biomedical APIs to validate entity types",
      "topics": [
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    },
    {
      "page": "query_mesh",
      "title": "Query for MeSH terms using E-utilities",
      "topics": [
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      ]
    },
    {
      "page": "query_umls",
      "title": "Query UMLS for term information",
      "topics": [
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      ]
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      "page": "run_lbd",
      "title": "Perform comprehensive literature-based discovery without type constraints",
      "topics": [
        "run_lbd"
      ]
    },
    {
      "page": "safe_diversify",
      "title": "Diversify ABC results with error handling",
      "topics": [
        "safe_diversify"
      ]
    },
    {
      "page": "sanitize_dictionary",
      "title": "Enhanced sanitize dictionary function",
      "topics": [
        "sanitize_dictionary"
      ]
    },
    {
      "page": "save_results",
      "title": "Save search results to a file",
      "topics": [
        "save_results"
      ]
    },
    {
      "page": "segment_sentences",
      "title": "Perform sentence segmentation on text",
      "topics": [
        "segment_sentences"
      ]
    },
    {
      "page": "valid_entities",
      "title": "Filter entities to include only valid biomedical terms",
      "topics": [
        "valid_entities"
      ]
    },
    {
      "page": "validate_abc",
      "title": "Apply statistical validation to ABC model results with support for large matrices",
      "topics": [
        "validate_abc"
      ]
    },
    {
      "page": "validate_biomedical_entity",
      "title": "Validate biomedical entities using BioBERT or other ML models",
      "topics": [
        "validate_biomedical_entity"
      ]
    },
    {
      "page": "validate_entity_comprehensive",
      "title": "Comprehensive entity validation using multiple techniques",
      "topics": [
        "validate_entity_comprehensive"
      ]
    },
    {
      "page": "validate_entity_with_nlp",
      "title": "Validate entity types using NLP-based entity recognition with improved accuracy",
      "topics": [
        "validate_entity_with_nlp"
      ]
    },
    {
      "page": "validate_umls_key",
      "title": "Validate a UMLS API key",
      "topics": [
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      ]
    },
    {
      "page": "vec_preprocess",
      "title": "Vectorized preprocessing of text",
      "topics": [
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    },
    {
      "page": "vis_abc_heatmap",
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