Network Verification Visual Glossary

BEL Language

BEL represents scientific knowledge in human readable and computable format as semantic triples (subject, a predicate, and an object).

Learn more about BEL Language version 1.0 Specifications on the Openbel.org page.

You can leverage the BELIEF platform for the transformation of unstructured scientific information into a structured, cause-effect representation in BEL language. The BELIEF platform automatically identifies causal relationships and proposes BEL statements.

Network definitions

  1. Node (e.g. p(HGNC:EGFR), bp(GO:"Oxidative Process"), a(CHEBI:Water)
  2. Network Edge - comprised of 1, 2 and 3 (node -> edge -> node)

Network Edge Examples:

  • p(HGNC:IL6) -> r(HGNC:ENO1)
  • p(HGNC:IL6) -> r(HGNC:XBP1)

Evidence - Supporting Statements

Network Edge: (1) -> (2)

3: Network Edge statement – each edge exists because there is at least one Evidence Statement that supports it, consisting of a node connected to another node by a relationship

4: Evidence statement - Each edge statement is supported by one or more Evidence (BEL) statements that provide the evidence for the edge, including the citation (usually PubMed ID) and experimental context

Evidence Statement

Evidence statement: protein abundance of CCND1 directly increases the kinase activity of the protein CDK4. Also shown is the Evidence statement context: Organism, Tissue, Cell and evidence for the Evidence (BEL) statement.

Causal Edge vs Non-causal Edge

Causal statements connect subject and object terms with a causal increase (symbol: -> ) or decrease (sympbol: -| ) relationship. Subject and object terms can be an abundance or process (including activities and transformations).

A non-causal statement is any statement that does not have an increase or decrease relationship (e.g. positiveCorrelation or association).

Groupthink Bias

Allowing members to unduly influence others in their actions and comments.

Reputation Actions

Actions on the NVC Site that lead to Reputation points. For example, adding Network Edges, Evidence and Evidence Annotations will provide reputation points to user. Up or down voting items that are created (Network Edges and Evidence) will also lead to reputation points.

The following table shows the points rewarded to a participant for the respective actions

Reputation Actions

Final score after Community responses to the actions

4 Votes approving

4 Votes rejecting

5 total Votes, but fewer than 4 approving/ rejecting

Less than 5 total Votes and fewer than 4 approving/ rejecting

Network Edge Creation (includes the points for the 1 st evidence creation for that edge)

100 points

0 points

5 points

5 points

Evidence Creation

50 points

0 points

5 points

5 points

Peer Approval

11-13 points (10 points plus one additional point for each response provided by the Entrant in the response form (excluding the “comments” section))

0 points

1-3 points (one point for each response provided by the Entrant in the response form (excluding the “comments” section))

1-3 points (one point for each response provided by the Entrant in the response form (excluding the “comments” section))

Peer Rejection

0 points

11-13 points (10 points plus one additional point for each response provided by the Entrant in the response form (excluding the “comments” section))

1-3 points (one point for each response provided by the Entrant in the response form (excluding the “comments” section))

1-3 points (one point for each response provided by the Entrant in the response form (excluding the “comments” section))