One major challenge in fully utilizing the vast public databases of biomedical literature is representing biological relationships in a standardized, computable format. The Biological Expression Language (BEL) [http://openBEL.org] is an emerging standard to capture and compile causal relationships while maintaining relevant contextual information as metadata. This presentation will provide an overview of the Biological Expression Language (BEL) format and how it has been applied in combination with the Cytoscape visualization software to construct a large array of biological networks. We will also highlight unique features of BEL that distinguish it from other prominent languages commonly utilized for network building.
The second part of the presentation will highlight the series of biological networks available as part of the ongoing Network Verification Challenge (NVC), a collaborative online project aimed at crowd-sourcing the improvement of biological network models for use by the global scientific community. We will review the foundation of language architecture using specific examples derived from the NVC project, as well as highlight the large array of visualization styles possible for language representation within networks. This tutorial will familiarize users with language semantics and demonstrate its broad applicability to scientific knowledge representation within the analytics community.