dashboard
Definition
A dashboard is an integrated visual interface that consolidates multiple data views, metrics, and analytical outputs into a single, interactive display for monitoring and decision-making. In life sciences research, dashboards aggregate heterogeneous biological data—including omics datasets, experimental results, literature findings, and computational predictions—enabling researchers to identify patterns, track experimental progress, and generate hypotheses. Effective dashboards combine static visualizations with dynamic, interactive elements that allow filtering, drill-down analysis, and real-time updates. They serve as centralized hubs for data exploration, facilitating the integration of disparate data sources and providing contextual information that supports evidence-based research decisions across drug discovery, clinical studies, and systems biology investigations.
Visualize dashboard in Nodes Bio
Nodes Bio enables researchers to create network-based dashboards that integrate multiple biological layers simultaneously. Users can visualize protein-protein interactions alongside gene expression data, pathway enrichment results, and disease associations in a unified graph interface. Interactive filtering and node expansion allow dynamic exploration of connected biological entities, while custom layouts highlight key regulatory hubs or therapeutic targets within complex multi-omics datasets.
Visualization Ideas:
- Multi-layer network showing gene expression, protein interactions, and metabolic pathways in integrated view
- Disease-centric dashboard connecting genetic variants, affected pathways, and potential drug targets
- Time-series network dashboard tracking dynamic changes in signaling cascades across experimental conditions
Example Use Case
A pharmaceutical team investigating Alzheimer's disease creates a dashboard integrating transcriptomic data from patient brain samples, known drug-target interactions, and protein aggregation pathways. The dashboard displays differentially expressed genes as network nodes, colored by fold-change and sized by statistical significance. Connected pathway modules reveal disrupted synaptic signaling and neuroinflammation clusters. By filtering for druggable targets with existing compounds, researchers identify three repurposing candidates that modulate multiple disease-relevant pathways, prioritizing them for preclinical validation based on network centrality metrics.