labeling
Definition
Labeling in biological network analysis refers to the assignment of identifiers, annotations, or attributes to nodes and edges to convey biological meaning and context. This includes gene symbols, protein names, functional classifications (GO terms), pathway memberships, expression levels, or interaction types. Effective labeling transforms abstract network structures into interpretable biological models by linking graph elements to experimental data, literature knowledge, and ontological frameworks. Labeling is critical for hypothesis generation, pattern recognition, and communicating findings, as it allows researchers to quickly identify key players, functional modules, and regulatory relationships within complex biological systems.
Visualize labeling in Nodes Bio
In Nodes Bio, researchers can apply multi-layered labeling to network nodes representing genes, proteins, or metabolites with annotations from databases like UniProt, GO, or KEGG. Custom labels can display expression fold-changes, disease associations, or drug targets directly on the visualization. Dynamic labeling filters enable users to highlight specific functional categories or pathways, making it easier to identify clusters of related biological entities and interpret network topology in the context of experimental results.
Visualization Ideas:
- Protein interaction networks with GO term labels color-coded by biological process
- Gene regulatory networks labeled with transcription factor binding confidence scores
- Metabolic pathways with enzyme nodes labeled by tissue-specific expression levels
Example Use Case
A cancer researcher analyzing a protein-protein interaction network from tumor samples uses labeling to annotate nodes with oncogene/tumor suppressor classifications, mutation frequencies, and drug target status. By applying color-coded labels for pathway membership (cell cycle, apoptosis, DNA repair), they identify a densely connected cluster of cell cycle regulators with high mutation rates. Additional labels showing FDA-approved drug targets within this cluster reveal potential combination therapy opportunities, guiding subsequent experimental validation of synergistic drug pairs.