similarity

Definition

Similarity in biological networks refers to quantitative measures of resemblance between entities such as genes, proteins, compounds, or biological pathways based on shared characteristics, structural features, functional properties, or expression patterns. Common similarity metrics include sequence similarity (BLAST scores), structural similarity (RMSD), functional similarity (Gene Ontology semantic similarity), and expression correlation (Pearson or Spearman coefficients). Similarity measures are fundamental for clustering analysis, target identification, drug repurposing, and predicting functional relationships. High similarity scores often indicate evolutionary relationships, functional redundancy, or potential for cross-reactivity, making similarity analysis essential for hypothesis generation and knowledge transfer across biological systems.

Visualize similarity in Nodes Bio

Researchers can visualize similarity relationships as weighted network edges, where edge thickness represents similarity strength between nodes. Nodes Bio enables clustering of highly similar entities, identification of functional modules, and exploration of compound-target relationships based on chemical or structural similarity. Users can filter networks by similarity thresholds to reveal the most relevant connections and identify potential drug candidates or functional orthologs.

Example Use Case

A pharmaceutical team investigating kinase inhibitors uses similarity analysis to identify compounds with structural resemblance to known drugs. By calculating Tanimoto coefficients between chemical structures, they construct a similarity network where compounds cluster by scaffold type. This reveals that a failed diabetes drug shares 85% structural similarity with an approved cancer therapeutic, suggesting potential for repurposing. The network visualization highlights unexpected similarity bridges between different therapeutic classes, guiding rational drug design efforts.

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