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LBDiscover is an R package for literature-based discovery (LBD) inbiomedical research. It provides a comprehensive suite of tools forretrieving scientific articles, extracting biomedical entities, buildingco-occurrence networks, and applying various discovery models to uncoverhidden connections in the scientific literature.
The package implements several literature-based discovery approachesincluding:
ABC model (Swanson’s discovery model)
AnC model (improved version with better biomedical term filtering)
Latent Semantic Indexing (LSI)
BITOLA-style approaches
LBDiscover also features powerful visualization tools for exploringdiscovered connections using networks, heatmaps, and interactivediagrams.
Installation
# Install from CRANinstall.packages("LBDiscover")# Or install the development version from GitHub# install.packages("devtools")devtools::install_github("chaoliu-cl/LBDiscover")
Key Features
LBDiscover provides a complete workflow for literature-based discovery:
Data Retrieval: Query and retrieve scientific articles fromPubMed and other NCBI databases
Text Preprocessing: Clean and prepare text for analysis
Entity Extraction: Identify biomedical entities in text(diseases, drugs, genes, etc.)
Co-occurrence Analysis: Build networks of entity co-occurrences
Discovery Models: Apply various discovery algorithms to findhidden connections
Validation: Validate discoveries through statistical tests
Visualization: Explore results through network graphs, heatmaps,and more
Quick Start Example
library(LBDiscover)# Retrieve articles from PubMedarticles<- pubmed_search("migraine treatment",max_results=100)# Preprocess article textpreprocessed<- vec_preprocess(articles,text_column="abstract",remove_stopwords=TRUE)# Extract biomedical entitiesentities<- extract_entities_workflow(preprocessed,text_column="abstract",entity_types= c("disease","drug","gene"))# Create co-occurrence matrixco_matrix<- create_comat(entities,doc_id_col="doc_id",entity_col="entity",type_col="entity_type")# Apply the ABC model to find new connectionsabc_results<- abc_model(co_matrix,a_term="migraine",n_results=50,scoring_method="combined")# Visualize the resultsvis_abc_network(abc_results,top_n=20)
Discovery Models
ABC Model
The ABC model is based on Swanson’s discovery paradigm. If concept A isrelated to concept B, and concept B is related to concept C, but A and Care not directly connected in the literature, then A may have a hiddenrelationship with C.
# Apply the ABC modelabc_results<- abc_model(co_matrix,a_term="migraine",min_score=0.1,n_results=50)# Visualize as a networkvis_abc_network(abc_results)# Or as a heatmapvis_heatmap(abc_results)
AnC Model
The AnC model is an extension of the ABC model that uses multiple Bterms to establish stronger connections between A and C.
# Apply the AnC modelanc_results<- anc_model(co_matrix,a_term="migraine",n_b_terms=5,min_score=0.1)
LSI Model
The Latent Semantic Indexing model identifies semantically related termsusing dimensionality reduction techniques.
If you use LBDiscover in your research, please cite:
Liu, C. (2025). LBDiscover: Literature-Based Discovery Tools for Biomedical Research. R package version 0.1.0. https://github.com/chaoliu-cl/LBDiscover
License
This project is licensed under the GPL-3 License - see the LICENSE filefor details.
About
Literature-Based Discovery Tools for Biomedical Research
