saliency map

Definition

A saliency map is a visual representation that highlights the most important or influential features, nodes, or regions within a dataset or model that contribute to a specific outcome or prediction. In biological networks, saliency maps identify which genes, proteins, or pathways have the greatest impact on a phenotype, disease state, or experimental result. These maps are often generated using machine learning interpretability methods, gradient-based techniques, or statistical scoring to quantify feature importance. Saliency maps enable researchers to focus on the most relevant biological components, reducing complexity in large-scale omics data and guiding hypothesis generation for mechanistic studies.

Visualize saliency map in Nodes Bio

Researchers can overlay saliency scores onto network graphs to visually emphasize high-impact nodes and edges. In Nodes Bio, color intensity, node size, or edge thickness can represent saliency values, making critical pathways immediately apparent. This allows teams to quickly identify key regulatory hubs, bottleneck proteins, or driver genes within complex biological networks, facilitating targeted experimental validation and drug target prioritization.

Example Use Case

A cancer genomics team uses deep learning to predict patient response to immunotherapy from multi-omics data. They generate a saliency map to identify which genes most strongly influence the model's predictions. The map reveals that specific immune checkpoint genes and interferon signaling pathway members have the highest saliency scores. By visualizing these findings as a network in Nodes Bio, the team discovers previously unrecognized gene interactions that explain resistance mechanisms, leading to combination therapy hypotheses targeting multiple high-saliency nodes simultaneously.

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