R: Literature-Based Discovery Tools for Biomedical Research

# Literature-Based Discovery Tools for Biomedical Research

## Documentation for package ‘LBDiscover’ version 0.1.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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