Bayesian networks
Definition
Bayesian networks are probabilistic graphical models that represent conditional dependencies between variables using directed acyclic graphs (DAGs). Each node represents a variable (gene, protein, phenotype), while edges indicate probabilistic dependencies with associated conditional probability distributions. In biological research, Bayesian networks excel at modeling complex causal relationships, inferring gene regulatory networks, predicting disease outcomes, and integrating heterogeneous data types. They handle uncertainty inherently through probability theory, making them valuable for noisy biological data. Unlike correlation-based methods, Bayesian networks can distinguish direct from indirect relationships and infer causal directions, enabling researchers to understand mechanistic pathways and predict intervention effects in biological systems.
Visualize Bayesian networks in Nodes Bio
Researchers can visualize Bayesian network structures as directed graphs where nodes represent biological entities and edges show probabilistic dependencies. Nodes Bio enables exploration of conditional probability relationships, identification of key regulatory nodes with high connectivity, and comparison of network structures across different conditions or datasets. Users can overlay probability values on edges and highlight causal pathways to understand disease mechanisms or drug response predictions.
Visualization Ideas:
- Gene regulatory networks with conditional probability edges showing transcription factor-target relationships
- Disease progression models with nodes for clinical variables and molecular markers connected by probabilistic dependencies
- Drug response prediction networks linking genetic variants to pharmacological outcomes through intermediate molecular phenotypes
Example Use Case
A cancer genomics team constructs a Bayesian network to model relationships between genetic mutations, gene expression changes, and patient survival outcomes in breast cancer. The network reveals that TP53 mutations directly influence expression of downstream apoptosis genes, which in turn affect treatment response. By querying the network with specific mutation profiles, researchers predict which patients will respond to targeted therapies. The probabilistic framework accounts for measurement noise and missing data, providing confidence estimates for each predicted relationship and identifying novel therapeutic targets.