knowledge representation
Definition
Knowledge representation is the systematic encoding of biological information, relationships, and rules into structured formats that enable computational reasoning and analysis. In life sciences, this involves organizing data about genes, proteins, pathways, diseases, and their interactions into formal frameworks such as ontologies, knowledge graphs, and semantic networks. Effective knowledge representation captures not just entities but also their properties, hierarchical relationships, and functional associations. It enables researchers to integrate heterogeneous data sources, perform automated inference, identify hidden connections, and generate testable hypotheses. Key approaches include Gene Ontology annotations, pathway databases like KEGG, and biomedical knowledge graphs that link molecular mechanisms to clinical phenotypes.
Visualize knowledge representation in Nodes Bio
Nodes Bio transforms knowledge representation into interactive network visualizations where biological entities become nodes and their relationships become edges. Researchers can visualize ontological hierarchies, map disease-gene-drug associations, and explore multi-layered knowledge graphs. The platform enables querying across integrated data sources, revealing non-obvious connections between molecular pathways and phenotypes, and supporting hypothesis generation through visual pattern recognition in complex biological knowledge networks.
Visualization Ideas:
- Multi-layer knowledge graphs connecting genes, proteins, pathways, and diseases
- Ontology hierarchies showing parent-child relationships in biological classifications
- Drug-target-disease networks integrating pharmacological and clinical knowledge
Example Use Case
A pharmaceutical team investigating Alzheimer's disease uses knowledge representation to integrate data from multiple sources: protein interaction databases, gene expression studies, clinical trial results, and drug target information. By visualizing this integrated knowledge as a network in Nodes Bio, they discover that three seemingly unrelated drug candidates all modulate proteins within two degrees of separation from APP processing enzymes. This network-based insight reveals a previously unrecognized mechanistic cluster, leading to a novel combination therapy hypothesis that targets multiple nodes in the amyloid cascade simultaneously.