Table 1.

Recently developed network enrichment tools categorized by algorithm type. Links to code/tools are provided in Supplementary Table 1.

Tool	Input data type (s)	Algorithm Type	Example application	Reference
SigMod	GWAS	Aggregate score	Identify functionally and biologically relevant genes in childhood-onset asthma[18]	[18]
IODNE	Gene expression	Aggregate score	Identify potentially novel target genes for drug selection in triple-negative breast cancer[19]	[19]
PCSF	Multi-omics data (gene expression, mutation profiles, or copy number)	Aggregate score	Extract subnetworks of enriched metabolite interactions in multiple sclerosis[36]	[20]
Omics Integrator	Gene expression	Aggregate score	Link α-synuclein to multiple parkinsonism genes and druggable targets[37]	[21]
MuST	Disease-associated genes (derived from GWAS or DEG analyses)	Aggregate score	Investigation of coagulation pathway in COVID-19[38]	[22]
ROBUST	Disease-associated genes (derived from GWAS or DEG analyses)	Aggregate score	Identify an oxidative stress module in multiple sclerosis[23]	[23]
DOMINO	Disease-associated genes (derived from GWAS or DEG analyses)	Aggregate score	Integrated as the downstream analysis step in a splicing-aware framework for time course data analysis[39]	[24]
KeyPathwayMiner	Gene expression / multi-omics data	Module cover	Reveal epigenetic targets in SARS-CoV-2 infection, used together with gene co-expression networks[40]	[25]
ModuleDiscoverer	Gene expression	Module cover	Identify regulatory modules of the response to mesenchymal stromal cells treatment for mitigating liver damage[41]	[26]
NoMAS	Mutation profiles	Module cover	Identify subnetworks with strong association to survival in different cancer types[27]	[27]
nCOP	Mutation profiles	Module cover	Identify cancer genes including those with low mutation frequencies across 24 different cancer types[28]	[28]
NetDecoder	Gene expression; mutation profiles	Score propagation	Studying mechanism of CCN1-associated resistance to HSV-1-derived oncolytic immunovirotherapies for glioblastomas[42]	[29]
HotNet2	Mutation profiles	Score propagation	Identify mutated subnetworks in a genome-scale interaction network used for subtyping pancreatic cancer[43]	[30]
Hierarchical HotNet	Mutation profiles	Score propagation	Detect functional interactions that connect the cellular targets of viral proteins with the downstream changes in SARS-CoV and SARS-CoV2[44]	[31]
Grand Forest	Gene expression / methylation data	Machine learning	Stratify lung cancer patients into clinically relevant molecular subgroups[32]	[32]
N2V-HC	GWAS	Machine learning	Discover biologically meaningful modules related to pathways underlying Parkinson's disease and Alzheimer's diseases[33]	[33]
BiCoN	Gene expression / methylation data	Machine learning	Applied to TCGA breast cancer data for subtyping[34]	[34]
TiCoNE	Time-series expression data	Machine learning	Analysis of time-series lipidomics dataset of human mesenchymal stem cells after drug treatment[45]	[35]