prior
Definition
In Bayesian statistics and causal inference, a prior (or prior probability distribution) represents existing knowledge or beliefs about a parameter before observing new data. In biological network analysis, priors incorporate domain knowledge such as known protein interactions, gene regulatory relationships, or pathway structures to guide model inference. Priors can be informative (based on literature, databases, or previous experiments) or uninformative (assuming minimal prior knowledge). They are crucial for network reconstruction, where they help distinguish true biological relationships from spurious correlations by weighting evidence according to biological plausibility. The strength of priors affects how much existing knowledge influences final conclusions versus letting data drive discoveries.
Visualize prior in Nodes Bio
Researchers can visualize how prior knowledge shapes network inference by comparing networks built with different prior assumptions. Nodes Bio enables overlaying literature-derived priors (from databases like STRING or KEGG) onto inferred networks, highlighting edges supported by existing evidence versus novel predictions. Users can adjust prior strength parameters and visualize how network topology changes, helping identify robust connections versus prior-dependent relationships in pathway reconstruction or gene regulatory network analysis.
Visualization Ideas:
- Network comparison showing prior-informed versus data-driven edge predictions with confidence scoring
- Heatmap overlay displaying prior strength from multiple knowledge sources on inferred regulatory networks
- Dynamic network animation showing how posterior networks evolve as prior assumptions are systematically varied
Example Use Case
A systems biology team investigating inflammatory signaling uses RNA-seq data to infer gene regulatory networks in macrophages. They incorporate priors from ChIP-seq databases indicating known transcription factor binding sites and protein-protein interaction databases. By visualizing networks with strong versus weak priors in Nodes Bio, they identify novel regulatory connections that emerge only when priors are relaxed, leading to discovery of an unexpected feedback loop involving NF-κB and a previously uncharacterized long non-coding RNA that modulates inflammatory response.