Disease Knowledge Base
This activity in eTRIKS describes an effort to construct an integrated knowledge repository to provide biological context to data emerging from experimental studies for selected IMI projects. We envisage the disease areas to be respiratory, neurological and possibly rheumatoid arthritis. The project has three main aims:
- To construct networks that integrate multiple heterogeneous biological data sources to provide a database of background knowledge
- To construct detailed expert-verified metabolic and signalling pathways known to be involved in the disease condition
- To enable sharing and community curation of of these disease knowledge representations, using the NDex or other appropriate frameworks.
We anticipate that these research efforts will facilitate i) interpretation of new experimental data (such as differentially expressed genes) in the context of existing background knowledge such as pathways and protein interactions, ii) hypothesis generation by enabling mining of the complex networks to look for new unexpected relationships and iii) exploration of disease co-morbidity in terms of molecular mechanism.
Disease Networks
The Disease Networks module is been developed as a multi-scale framework (using the graph datagabse approach) to facilitate management (integration, exploration, visualisation, interpretation) of diverse types of biological and biomedical data. Disease Networks employ the popular graph database Neo4j, which provides a persistence mechanism that is robust and has powerful functionality (the Cypher query language) that allows the user to query networks, to find connections between particular data entries using graph traversal techniques.