Bayesian update
Definition
Bayesian update is a statistical method for revising probability estimates as new evidence becomes available, based on Bayes' theorem. In biological research, it enables iterative refinement of hypotheses about molecular interactions, disease mechanisms, or drug effects by combining prior knowledge with experimental data. The process calculates posterior probabilities by multiplying prior beliefs with likelihood ratios from new observations. This approach is particularly valuable in systems biology and network inference, where researchers must integrate diverse data sources—genomics, proteomics, clinical observations—to build increasingly accurate models of biological systems. Bayesian updating provides a principled framework for quantifying uncertainty and making predictions that improve as evidence accumulates.
Visualize Bayesian update in Nodes Bio
In Nodes Bio, researchers can visualize how network confidence evolves through Bayesian updates by displaying edge weights or node probabilities that change as new experimental data is incorporated. Color gradients or edge thickness can represent posterior probabilities, showing which pathway connections become more or less certain. This allows teams to track how protein-protein interaction networks or gene regulatory models are refined through sequential experiments.
Visualization Ideas:
- Dynamic network graphs showing edge confidence changes across sequential experiments
- Heat maps overlaying posterior probabilities on protein interaction networks
- Time-series networks displaying how gene regulatory relationships strengthen or weaken with accumulating evidence
Example Use Case
A pharmaceutical team investigating Alzheimer's disease starts with a protein interaction network based on literature mining (prior). As they conduct proteomics experiments on patient brain tissue, they perform Bayesian updates to refine interaction probabilities. Initially uncertain connections between tau protein and specific kinases become strongly supported or eliminated. After integrating RNA-seq data from multiple cohorts, their network model's posterior probabilities guide selection of the most promising therapeutic targets, with high-confidence nodes prioritized for drug screening.