Upload at 2019-03-15 15:44:07.554265
Sandra Spalek
CC BY 4.0
Copyright © 2019 Fraunhofer Institute SCAI, All rights reserved.
Number Nodes
Number Edges
Number Components
Network Density
Average Degree
Number Citations
Number BEL Errors

Content Statistics

Network Overlap

The node-based overlap between this network and other networks is calculated as the Szymkiewicz-Simpson coefficient of their respective nodes. Up to the top 10 are shown below.

Network Overlap
Tau Trimers Are the Minimal Propagation Unit Spontaneously Internalized to Seed Intracellular Aggregation v1.0.0 36%
Tau Modifications v1.9.5 35%
Identification of the Tau phosphorylation pattern that drives its aggregation v1.0.0 31%
Anti-aggregant tau mutant promotes neurogenesis v1.0.0 30%
Tau in physiology and pathology v1.0.0 30%
Inert and seed-competent tau monomers suggest structural origins of aggregation v1.0.0 25%
Alzheimer’s disease and the autophagic-lysosomal system v1.0.0 24%
The Ubiquitin–Proteasome System and the Autophagic–Lysosomal System in Alzheimer Disease v1.0.0 24%
Caenorhabditis elegans models of tauopathy v1.0.0 22%
Nuclear Factor Kappa-light-chain-enhancer of Activated B Cells (NF-κB) - a Friend, a Foe, or a Bystander - in the Neurodegenerative Cascade and Pathogenesis of Alzheimer's Disease v1.0.0 22%

Sample Edges

a(HBP:"Tau aggregates") causesNoChange bp(GO:"cell death") View Subject | View Object

In our hands, these aggregated tau species formed in different conditions did not show any significant release of LDH when applied on the differentiated SH-SY5Y cells (Supplementary Fig. 8A), and they did not show a significant reduction in cell viability by the MTTassay (Supplementary Fig. 8B). PubMed:28528849

Experimental Factor Ontology (EFO)

Sample Nodes


In-Edges: 19 | Out-Edges: 25 | Explore Neighborhood | Download JSON


In-Edges: 112 | Out-Edges: 33 | Explore Neighborhood | Download JSON


In-Edges: 477 | Out-Edges: 480 | Classes: 11 | Children: 27 | Explore Neighborhood | Download JSON


BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of PyBEL, an open source project. Please feel free to contact us here to give us feedback or report any issues. Also, see our Publishing Notes and Data Protection information.

If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.