Literature-Based Discovery Tools for Biomedical Research

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Documentation for package ‘LBDiscover’ version 0.1.0

• DESCRIPTION file.
• Package NEWS.

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abc_model	Apply the ABC model for literature-based discovery with improved filtering
abc_model_opt	Optimize ABC model calculations for large matrices
abc_model_sig	Apply the ABC model with statistical significance testing
abc_timeslice	Apply time-sliced ABC model for validation
anc_model	ANC model for literature-based discovery with biomedical term filtering
bitola_model	Apply BITOLA-style discovery model
calc_bibliometrics	Calculate basic bibliometric statistics
calc_doc_sim	Calculate document similarity using TF-IDF and cosine similarity
clear_pubmed_cache	Clear PubMed cache
cluster_docs	Cluster documents using K-means
compare_terms	Compare term frequencies between two corpora
create_citation_net	Create a citation network from article data
create_comat	Create co-occurrence matrix without explicit entity type constraints
create_report	Generate a comprehensive discovery report
create_sparse_comat	Create a sparse co-occurrence matrix
create_tdm	Create a term-document matrix from preprocessed text
create_term_document_matrix	Create a term-document matrix from preprocessed text
detect_lang	Detect language of text
diversify_abc	Enforce diversity in ABC model results
enhance_abc_kb	Enhance ABC results with external knowledge
eval_evidence	Evaluate literature support for discovery results
export_chord	Export interactive HTML chord diagram for ABC connections
export_chord_diagram	Export interactive HTML chord diagram for ABC connections
export_network	Export ABC results to simple HTML network
extract_entities	Extract and classify entities from text with multi-domain types
extract_entities_workflow	Extract entities from text with improved efficiency using only base R
extract_ner	Perform named entity recognition on text
extract_ngrams	Extract n-grams from text
extract_terms	Extract common terms from a corpus
extract_topics	Apply topic modeling to a corpus
filter_by_type	Filter a co-occurrence matrix by entity type
find_abc_all	Find all potential ABC connections
find_similar_docs	Find similar documents for a given document
find_term	Find primary term in co-occurrence matrix
gen_report	Generate comprehensive discovery report
get_dict_cache	Get dictionary cache environment
get_pmc_fulltext	Retrieve full text from PubMed Central
get_term_vars	Extract term variations from text corpus
get_type_dist	Get entity type distribution from co-occurrence matrix
is_valid_biomedical_entity	Determine if a term is likely a specific biomedical entity with improved accuracy
load_dictionary	Load biomedical dictionaries with improved error handling
load_results	Load saved results from a file
lsi_model	LSI model with enhanced biomedical term filtering and NLP verification
map_ontology	Map terms to biomedical ontologies
merge_entities	Combine and deduplicate entity datasets
merge_results	Merge multiple search results
min_results	Ensure minimum results for visualization
ncbi_search	Search NCBI databases for articles or data
parallel_analysis	Apply parallel processing for document analysis
perm_test_abc	Perform randomization test for ABC model
plot_heatmap	Create heatmap visualization from results
plot_network	Create network visualization from results
preprocess_text	Preprocess article text
prep_articles	Prepare articles for report generation
pubmed_search	Search PubMed for articles with optimized performance
query_external_api	Query external biomedical APIs to validate entity types
query_mesh	Query for MeSH terms using E-utilities
query_umls	Query UMLS for term information
run_lbd	Perform comprehensive literature-based discovery without type constraints
safe_diversify	Diversify ABC results with error handling
sanitize_dictionary	Enhanced sanitize dictionary function
save_results	Save search results to a file
segment_sentences	Perform sentence segmentation on text
validate_abc	Apply statistical validation to ABC model results with support for large matrices
validate_biomedical_entity	Validate biomedical entities using BioBERT or other ML models
validate_entity_comprehensive	Comprehensive entity validation using multiple techniques
validate_entity_with_nlp	Validate entity types using NLP-based entity recognition with improved accuracy
validate_umls_key	Validate a UMLS API key
valid_entities	Filter entities to include only valid biomedical terms
vec_preprocess	Vectorized preprocessing of text
visualize_abc_network	Visualize ABC model results as a network
vis_abc_heatmap	Create a heatmap of ABC connections
vis_abc_network	Visualize ABC model results as a network
vis_heatmap	Create an enhanced heatmap of ABC connections
vis_network	Create an enhanced network visualization of ABC connections