tbkg

is a Knowledge Graph.

A weighted heterogeneous knowledge graph containing four types of entities (Tumor, Biomarker, Drug, and ADR) extracted from MEDLINE corpus for adverse drug reaction discovery in antitumor drugs. TBKG uses a naive Bayesian model to explore correlations and provides explainable predictions through tumor-biomarker-drug pathways. The knowledge graph contains 1,179 tumors, 2,550 biomarkers, 1,806 drugs, and 756 ADRs with six types of relationships totaling 139,254 edges.

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Infores ID

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FAIRsharing ID

Unknown

Product Summary

Publications

Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph

Products

From this Resource

ID	Name	URL	Category	Format	Description
tbkg.data	TBKG Knowledge Graph Data	full#supplementary-material	GraphProduct	mixed	Weighted heterogeneous knowledge grap...
tbkg.osimertinib_case_study	TBKG Osimertinib ADR Case Study Data	full#supplementary-material	Product	mixed	Clinical validation dataset with calc...

Relevant Taxa

Human (NCBITaxon:9606)

Details

Overview

TBKG (Tumor-Biomarker Knowledge Graph) is an explainable knowledge graph-based approach for discovering potential adverse drug reactions (ADRs) of antitumor drugs. The system extracts entities from biomedical literature (MEDLINE database with 22+ million citations) using the UMLS Metathesaurus 2020AA and Apache cTAKES natural language processing tool.

Key Features

Entity Types: Four node types (Tumor, Biomarker, Drug, ADR) with minimum frequency threshold of 50
Knowledge Graph Structure: Weighted heterogeneous graph with undirected edges representing correlations
Relationship Discovery: Naive Bayesian model combining prior and posterior probabilities to avoid bias
ADR Discovery: Depth-first search algorithm to find all paths between drug-ADR pairs
Explainability: Provides tumor-biomarker-drug pathways explaining predicted ADRs
Performance: 0.81 accuracy in three-fold cross-validation, outperforms co-occurrence analysis

Data Sources

MEDLINE database (1928-2020, filtered with “cancer therapy” keyword)
UMLS Metathesaurus 2020AA for entity dictionaries
WHO source dictionary for ADR coding
Clinical validation from 3rd Xiangya Hospital (8 patients, 2017-2020)

Technical Implementation

Entity Extraction: cTAKES NLP system with UMLS concept mapping
Importance Measure: log(p(biomarker tumor present)) - log(p(biomarker tumor absent))
Graph Construction: Binary matrix representation (0/1 for entity presence in abstracts)
Cross-validation: Three-fold to prevent overfitting

Clinical Validation

Osimertinib case study demonstrated:

Moderate consistency with official manual (Kappa=0.68)
Better specificity than co-occurrence methods (Kappa=0.4)
Discovery of rare/unreported ADRs (e.g., renal failure requiring dialysis)
Identification of mediating biomarkers (e.g., Macrophage-Activating Factors for nephrosclerosis)

Applications

Early ADR detection before drug development
Mechanism research through biomarker pathways
Clinical decision support for oncologists
Literature-based drug safety surveillance
Rare ADR discovery and explanation