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Network Gene Essentiality Predictor
NGEP combines WikiPathways and Reactome biological network analysis with XGBoost machine learning to predict gene essentiality from network topology — cascade size, betweenness, sink reach and more.
2,079
WikiPathways GPML files
Multi-organism curated biological pathways across 40+ species
754
Reactome GPML files
Human-focused, expert-curated signalling & metabolic reactions
2,833
Total Pathways
—
Indexed Genes
19,278
Known Lethal Genes
—
Organisms Covered
Quick Gene Search
Search across all 2,833 pathways and get essentiality predictions instantly.
How It Works
1
Dual-Database Pathway Parsing
GPML files from WikiPathways (2,079) and Reactome (754) are parsed as directed gene interaction graphs.
2,079 WP pathways
754 Reactome pathways
20+ organisms
2
Network Feature Extraction
8 topological features computed per gene node from graph analysis.
Out-degree / In-degree
Cascade Size & Sink Reach
Betweenness / Closeness / Eigenvector
Relative Impact Score
Cascade Size & Sink Reach
Betweenness / Closeness / Eigenvector
Relative Impact Score
3
XGBoost ML Prediction
A gradient-boosted classifier trained on DEG database lethal/non-lethal labels predicts gene essentiality scores.
Score ≥ 0.6 → Lethal
19,278 verified lethal genes
36,856 non-lethal genes
19,278 verified lethal genes
36,856 non-lethal genes
Pathway Coverage by Organism
Database Source Split