  Transcriptomic data is a treasure-trove in modern molecular biology, as it
offers a comprehensive viewpoint into the intricate nuances of gene expression
dynamics underlying biological systems. This genetic information must be
utilised to infer biomolecular interaction networks that can provide insights
into the complex regulatory mechanisms underpinning the dynamic cellular
processes. Gene regulatory networks and protein-protein interaction networks
are two major classes of such networks. This chapter thoroughly investigates
the wide range of methodologies used for distilling insightful revelations from
transcriptomic data that include association based methods (based on
correlation among expression vectors), probabilistic models (using Bayesian and
Gaussian models), and interologous methods. We reviewed different approaches
for evaluating the significance of interactions based on the network topology
and biological functions of the interacting molecules, and discuss various
strategies for the identification of functional modules. The chapter concludes
with highlighting network based techniques of prioritising key genes, outlining
the centrality based, diffusion based and subgraph based methods. The chapter
provides a meticulous framework for investigating transcriptomic data to
uncover assembly of complex molecular networks for their adaptable analyses
across a broad spectrum of biological domains.
