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LBDiscover

Overview

LBDiscover is an R package for literature-based discovery (LBD) in biomedical research. It provides a comprehensive suite of tools for retrieving scientific articles, extracting biomedical entities, building co-occurrence networks, and applying various discovery models to uncover hidden connections in the scientific literature.

The package implements several literature-based discovery approaches including:

• 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 exploring discovered connections using networks, heatmaps, and interactive diagrams.

Installation

# Install from CRAN
install.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:

1. Data Retrieval: Query and retrieve scientific articles from PubMed and other NCBI databases
2. Text Preprocessing: Clean and prepare text for analysis
3. Entity Extraction: Identify biomedical entities in text (diseases, drugs, genes, etc.)
4. Co-occurrence Analysis: Build networks of entity co-occurrences
5. Discovery Models: Apply various discovery algorithms to find hidden connections
6. Validation: Validate discoveries through statistical tests
7. Visualization: Explore results through network graphs, heatmaps, and more

Quick Start Example

library(LBDiscover)

# Retrieve articles from PubMed
articles <- pubmed_search("migraine treatment", max_results = 100)

# Preprocess article text
preprocessed <- vec_preprocess(
  articles,
  text_column = "abstract",
  remove_stopwords = TRUE
)

# Extract biomedical entities
entities <- extract_entities_workflow(
  preprocessed,
  text_column = "abstract",
  entity_types = c("disease", "drug", "gene")
)

# Create co-occurrence matrix
co_matrix <- create_comat(
  entities,
  doc_id_col = "doc_id",
  entity_col = "entity",
  type_col = "entity_type"
)

# Apply the ABC model to find new connections
abc_results <- abc_model(
  co_matrix,
  a_term = "migraine",
  n_results = 50,
  scoring_method = "combined"
)

# Visualize the results
vis_abc_network(abc_results, top_n = 20)

Discovery Models

ABC Model

The ABC model is based on Swanson’s discovery paradigm. If concept A is related to concept B, and concept B is related to concept C, but A and C are not directly connected in the literature, then A may have a hidden relationship with C.

# Apply the ABC model
abc_results <- abc_model(
  co_matrix,
  a_term = "migraine",
  min_score = 0.1,
  n_results = 50
)

# Visualize as a network
vis_abc_network(abc_results)

# Or as a heatmap
vis_heatmap(abc_results)

AnC Model

The AnC model is an extension of the ABC model that uses multiple B terms to establish stronger connections between A and C.

# Apply the AnC model
anc_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 terms using dimensionality reduction techniques.

# Create term-document matrix
tdm <- create_term_document_matrix(preprocessed)

# Apply LSI model
lsi_results <- lsi_model(
  tdm,
  a_term = "migraine",
  n_factors = 100
)

Visualization

The package offers multiple visualization options:

# Network visualization
vis_abc_network(abc_results, top_n = 25)

# Heatmap of connections
vis_heatmap(abc_results, top_n = 20)

# Export interactive HTML network
export_network(abc_results, output_file = "abc_network.html")

# Export interactive chord diagram
export_chord(abc_results, output_file = "abc_chord.html")

Comprehensive Analysis

For an end-to-end analysis:

# Run comprehensive discovery analysis
discovery_results <- run_lbd(
  search_query = "migraine pathophysiology",
  a_term = "migraine",
  discovery_approaches = c("abc", "anc", "lsi"),
  include_visualizations = TRUE,
  output_file = "discovery_report.html"
)

Documentation

For more detailed documentation and examples, please see the package vignettes:

# View package vignettes
browseVignettes("LBDiscover")

Citation

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 file for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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