Table 1:

Overview of the types of networks often used in analytical analysis for network medicine applications.

Network Type	Structure	Nodes Represent	Edges Represent	Typically Derived From	Primary Use(s)
Protein-Protein Interaction	undirected; unipartite	proteins	physical or functional protein interactions	affinity purification, yeast-2-hybrid, tandem affinity purification	disease module detection
Gene Regulatory*	directed; bipartite	transcription factors and genes	regulation of genes by transcription factor proteins	DNA sequence scan, ChIP-assays, reverse-engineered from expression data	modeling and identification of alterations to regulatory processes
Correlation**	undirected; unipartite	genes/mRNAs	correlation in mRNA levels of genes	expression data	mining information about key genes and identifying drivers of gene expression
Bayesian	directed; acyclic	variables	dependency between variables	measurements across a series of variables	modeling relationships between variables
RNA-RNA	directed; multipartite	RNA species	regulation of RNA (e.g., lncRNA regulates miRNA; miRNA regulates mRNA)	expression data combined with regulatory information	identification of potential diagnostic biomarkers and therapeutic targets

^*Epigenomic data can be integrated into gene regulatory networks to construct epigenomic regulatory networks. This additional layer of information can aid in the identification of biomarkers and therapeutic targets.

^**Correlation networks can also be constructed between other types of biological molecules using other types of Omics data. For example, a correlation network of metabolites can be constructed using metabolomics data.