TABLE 1.

Summary of mechanism-centric methods discussed in this review.

Method	Data modality	Utilize knowledge base?
Gene co-expression network-based	Identify modules of highly correlated genes +Increased interpretability at the mechanistic level +Associate genes with previously uncharacterized biological functions –Directionality of gene-gene interactions is unknown
Centered Concordance Index (CCI) (Han et al., 2016)	Condition-specific module identification
	Single-omic	No
Eigengenes (Alter et al., 2000; Zhang and Horvath, 2005)	Identify modules associated with clinical features of interest
	Single-omic	No
Hubs (Freeman, 1977; Horvath and Dong, 2008)	Hub gene identification +Identify potential mechanism-centric target
	Single-omic	No
Regulatory network-based	Identify regulatory relationships between a TF/co-TF and its target genes +Increased interpretability at the mechanistic level +Identify potential drivers of disease +Can identify non-linear relationships +Tissue specific network
MARINa (Lefebvre et al., 2010)	Identify MRs from a set of samples containing two phenotypes –Need phenotype signature
	Single-omic	No
VIPER (Alvarez et al., 2016)	Single-sample MR identification from a cohort –Dataset scaling
	Single-omic	No
RegNetDriver (Dhingra et al., 2017)	Identify TF hubs that are significantly affected by single nucleotide variants, structural variants, or DNA methylation +Increase interpretability of TF hub activity through multi-omic integration –Limited by information in knowledge base
	Multi-omic	Yes
PPI network-based	Use PPI subnetworks as a functional unit +Increased interpretability at the mechanistic level +Connect results to the protein complex level –Limited by information in knowledge base
Chuang et al., 2007	Identify subnetworks with differential activity in metastatic breast cancer +Tissue-specificity from overlaying gene expression data +Improved biomarker classification accuracy and reproducibility –Dataset scaling
	Multi-omic	Yes
Pathway-based	Use molecular pathways as a functional unit +Increased interpretability at the mechanistic level –Limited by information in knowledge base
pathCHEMO (Epsi et al., 2019)	Identify significantly altered pathways (at transcript and DNA methylation levels) in response to chemotherapy in lung and colorectal cancer +Improved biomarker classification accuracy and reproducibility –Need phenotype signature
	Multi-omic	Yes
pathER (Rahem et al., 2020)	Identify pathways as markers of tamoxifen resistance in ER + breast cancer +Improved biomarker classification accuracy and reproducibility –Dataset scaling
	Single-omic	Yes

The objective of each method is detailed in italics, followed by their respective pros (+) and cons (–). Overall pros and cons for each method type are listed in a non-redundant manner. Information on data modality and if a method utilized a knowledge base is detailed as well.