Biological network methods are computational and statistical techniques used to construct, analyze, and interpret networks that represent biological entities (such as genes, proteins, metabolites, or cells) and their interactions or associations. These methods encompass network inference (e.g., from omics data using correlation, mutual information, or Bayesian approaches), network topology analysis (centrality, modularity, community detection), and network-based modeling of dynamics (e.g., Boolean or differential equation models). They are applied to identify functional modules, key regulators, and pathways, integrate multi-omics datasets, and support predictions of system behavior, disease mechanisms, and potential therapeutic targets within complex biological systems.
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