probabilistic graphical models
Definition
Probabilistic graphical models (PGMs) are mathematical frameworks that use graph structures to represent complex probability distributions over multiple variables. In these models, nodes represent random variables (genes, proteins, phenotypes) and edges encode probabilistic dependencies between them. PGMs include Bayesian networks (directed acyclic graphs) and Markov random fields (undirected graphs), enabling researchers to perform inference, predict outcomes, and discover causal relationships from biological data. They are particularly valuable in systems biology for modeling gene regulatory networks, protein interactions, and disease mechanisms where uncertainty and incomplete information are inherent, allowing integration of diverse data types while quantifying confidence in predictions.
Visualize probabilistic graphical models in Nodes Bio
Researchers can visualize probabilistic graphical models as interactive network graphs where node size represents variable importance, edge thickness indicates strength of probabilistic dependencies, and conditional probability tables are accessible through node inspection. This enables exploration of learned network structures from omics data, identification of key regulatory hubs, and visualization of inference pathways to understand how evidence propagates through biological systems.
Visualization Ideas:
- Bayesian network showing conditional dependencies between genes, proteins, and phenotypes with probability annotations
- Markov random field representing spatial gene expression patterns with undirected probabilistic relationships
- Factor graph displaying how genetic variants jointly influence disease risk through intermediate molecular phenotypes
Example Use Case
A cancer genomics team builds a Bayesian network to model relationships between genetic mutations, gene expression patterns, and patient survival outcomes across 500 tumor samples. The resulting PGM reveals that mutations in TP53 probabilistically influence expression of 47 downstream genes, which collectively predict treatment response with 82% accuracy. By visualizing this network, researchers identify three previously unknown gene modules that mediate the mutation's effect on chemotherapy resistance, prioritizing these pathways for therapeutic intervention.