Biological network methods

Computational and statistical techniques for constructing, analyzing, and interpreting networks of biological entities and their relationships.

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Graph theory: The mathematical study of graphs, structures made of vertices connected by edges. Provides the formal language for representing biological entities and their relationships.
Network inference: The estimation of network structure from observed data. It reconstructs relationships that experiments have not directly mapped.
Gene regulatory network: A network representing regulatory influences among genes and their products. Inference methods reconstruct how transcription factors and other regulators control genes.
Causal inference: Methods for estimating cause-and-effect relationships from data and assumptions. Associations in a network do not by themselves establish causal influence.
Biological network: A graph whose nodes and edges represent biological entities and relationships. Defines the objects these methods construct and analyze.
Correlation network: A network whose edges connect variables with statistically associated measurements. Correlation is a common route from expression data to candidate biological relationships.
Co-expression network: A network connecting genes whose expression levels vary together across samples. It groups genes into modules associated with conditions, tissues, or traits.
Structural equation modeling: A statistical framework for modeling relationships among observed and latent variables. It represents hypothesized dependencies without requiring a graph-centered network analysis.
Protein–protein interaction: A physical or functional association between two proteins. Protein interactions are a common relationship encoded in biological networks.
Bayesian network: A directed acyclic graph encoding conditional dependencies among variables. It models probabilistic dependencies and can incorporate prior biological knowledge.