The BEL programming language is parse-able into a computable format that allows transformations and expansions for building a computable knowledge graph which can then be used in algorithms such as Reverse Causal Reasoning, Heat Diffusion or as prior knowledge for deep learning.

BEL is designed to represent scientific findings by capturing causal and correlative relationships in context, where context can include information about the biological and experimental system in which the relationships were observed, the supporting publications cited and the process of curation.