Table 1.

Frequently used terms in network biology.

Term	Definition
Epigenetic	Epigenetic studies genetic effects not encoded in the DNA sequence of an organism.
Gene ontology (GO)	Gene ontology is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species.
Genome wide association study (GWAS)	A genome wide association study is an examination of the entire genome that is useful to identify genetic variants (SNPs) associated with a trait of interest.
Module	Module is defined as a group of physically or functionally linked molecules that work together to achieve a relatively distinct function. Modules are also called groups, clusters or communities. Examples of modules are co-regulation, co-expression, membership of a protein complex, of a metabolic or signaling pathway.
Network analysis	Network analysis is a method to systematically analyze a group of interconnected components. Nodes and edges are the basic components of a network. Nodes represent units in the network and edges represent the interactions between the units. Hubs are nodes with high connectivity.
Network medicine	Network medicine is an emerging field of network biology that applies the principles that govern cellular and molecular networks in the context of health and disease.
-omes	-omes are large scale networks. Interactome refers to the entire set of interactions in a particular cell. These interactions could represent, for example, protein-protein interactions (PPI) or interactions between messenger RNA molecules, also known as the transcriptome.
Single nucleotide polymorphism (SNP)	A single nucleotide polymorphism is a variation in a single nucleotide that occurs at a specific position in the genome. They are the most common type of genetic variation among people.
Weighted gene co-expression network analysis (WGCNA)	Weighted gene co-expression network analysis, also known as weighted correlation network analysis (WCNA), represents a systems biologic method for analyzing microarray data, gene information data, and microarray sample traits (e.g., case control status or clinical outcomes). WGCNA facilitates a network-based gene screening method that can be used to identify candidate biomarkers or therapeutic targets.