e-TSN

e-TSN maps target-disease associations from biomedical literature and constructs a knowledge graph with bibliometric-based significance and novelty scores to prioritize genes and proteins as therapeutic targets.

Key Features:

• Knowledge Graph Integration: Constructs a knowledge graph integrating heterogeneous biomedical data focused on associations between targets (genes and proteins) and diseases.
• Significance and Novelty Scoring: Computes significance and novelty scores from bibliometric statistics to prioritize candidate disease-related proteins and genes.
• Visualization Capabilities: Produces network visualizations of target-disease relationships and drug-target bioactivity links derived from the knowledge graph.
• Drug-Target Relationship Mapping: Links approved drugs and associated bioactivity data to targets within the knowledge graph.

Scientific Applications:

• Drug Discovery and Development: Prioritizes therapeutic targets by combining knowledge graph associations with bibliometric significance and novelty scores.
• Understanding Disease Mechanisms: Maps interactions between genes/proteins and diseases to support elucidation of disease mechanisms.
• Pandemic Response: Integrates emerging biomedical literature (e.g., COVID-19) to identify potential targets and relevant drug-target links during infectious disease outbreaks.

Methodology:

Applies text mining to extract information from a corpus of biomedical literature, integrates extracted knowledge into a cohesive database/knowledge graph, and derives significance and novelty scores using bibliometric statistics.