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EpistasisLab/AlzKB-BR

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AlzKB-BR: Extending the Alzheimer's Knowledge Base with Brain Region Transcriptomics for Interpretable Drug Discovery

Overview

AlzKB-BR extends the Alzheimer's Knowledge Base (AlzKB) by integrating brain region-specific transcriptomic information from the Allen Human Brain Atlas (ABA). The goal is to improve the biological interpretation of knowledge graph-based drug repurposing predictions for Alzheimer's disease (AD) by incorporating neuroanatomical context.


Study Workflow

1. Brain Region Data Integration

  • Obtain transcriptomic data from the Allen Human Brain Atlas (ABA)
  • Perform one-versus-all differential expression analysis using limma
  • Generate gene–brain region relationships
  • Extend AlzKB to create AlzKB-BR

2. Knowledge Graph Evaluation

  • Compare AlzKB and AlzKB-BR
  • Train the RotatE knowledge graph embedding model
  • Evaluate performance using MRR, Hits@1, Hits@3, and Hits@10

3. Drug Prediction and Prioritization

  • Predict missing Drug–Treats–Disease relationships
  • Aggregate predictions using Robust Rank Aggregation (RRA)
  • Apply statistical and biological filtering

4. Biological Interpretation

  • Interpret candidate drugs using AD-associated brain regions
  • Analyze associated gene sets using GeneAgent
  • Review supporting clinical trial and literature evidence

Repository Structure

.
├── data/
│   ├── Allen_Human_Brain_Atlas/
│   ├── AlzKB/
│   │   └── CYPHERL/
│   └── AlzKB-BR/
│       └── CYPHERL/
│
├── output/
│   ├── DEG_results/
│   ├── Validation_results/
│   └── Drug_selection_results/
│
├── scripts/
│   ├── data_acquisition_curation.R
│   ├── probe_to_ensembl_mapping.R
│   ├── differential_expression_analysis.R
│   ├── calculate_z_scores.R
│   ├── validate_rotate_model.py
│   ├── robust_rank_aggregation.R
│   ├── construct_brain_region_relationships.ipynb
│   └── biological_refinement.ipynb
│
├── figures/
├── supplementary/
└── README.md

Methods

Brain Region Integration

  • Allen Human Brain Atlas (ABA) transcriptomic data
  • Probe-to-gene mapping
  • Differential expression analysis using limma
  • BrainRegion ontology construction
  • Integration into AlzKB-BR

Knowledge Graph Completion

  • PyKEEN
  • RotatE knowledge graph embedding
  • Optuna hyperparameter optimization
  • 10 independent random seeds

Drug Prioritization

  • Robust Rank Aggregation (RRA)

  • Median + Median Absolute Deviation (MAD) filtering

  • Biological refinement

    • FDA-approved drugs
    • AD-associated genes
    • Brain-region relevance
    • Pharmacogene exclusion

Biological Interpretation

  • GeneAgent (GPT-4 based)
  • AD-associated brain regions
  • Drug indications
  • ClinicalTrials.gov
  • Literature review

Key Results

  • Added 330 BrainRegion nodes
  • Added 25,174 brain region-related relationships
  • Preserved RotatE knowledge graph completion performance
  • Identified 36 prioritized AD drug candidates
  • Identified 22 candidate drugs unique to AlzKB-BR
  • Improved biological interpretation through neuroanatomical context

Software

  • Python
  • R
  • Neo4j
  • PyKEEN
  • limma
  • Optuna
  • GeneAgent

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