By providing our users with an interactive and easy to use platform, we allow them to focus on what is really important: the science.
Build, view and share networks using a standard language BEL in an open-standard format JGF. Fully searchable network database, find networks by network nodes, attributes and associated metadata. Easy import and export of networks.
BioDati Studio is fully API-driven providing access to all functionality and documented using Swagger/OAS. This makes it easy to connect into analytics pipeline or your own platforms.
Condenses papers and databases into a contexualized and highly relevant biological relationships. BEL compresses biological literature into a highly readable and relatable format.
BEL (Biological Expression Language) is an open standard that represents biology from molecular to species-level scale which captures causal and non-causal relationships in biology with experimental context.
All curated biological knowledge gets converted into a graph database for searching pathways and other biology relationships for use in building networks or knowledge-driven analytics.
Biological Expression Language (BEL) is an open standard language for representing scientific findings in the life sciences in a computable form. 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 curation process used.
Nanopubs are the smallest unit of BEL knowledge. It is composed of BEL assertion(s), source citation, experimental context, other annotations, and associated metadata. Nanopubs are stored in a database with powerful text and data searching functionality. BioDati Studio provides the most advanced BEL Nanopub editor available to create and edit Nanopubs.
The database of edges created from curated biology (BEL Nanopubs) is used as a source of edges for network building (filterable by species, disease context, experimental conditions, …). You can select a set of edges to insert into a new network or add to an existing network. For example, find all human edges, 2 steps out from the protein AKT1 derived from an experiment using lung epithelial tissue.
Networks created from BEL edges represent specific biological systems and/or states. An example network could represent all known biological knowledge for a particular mechanism, such as apoptosis in humans, where each edge in the network is contextualized by edge metadata from one or more BEL Nanopubs.