Table 3.

Overview of analysis methods for network biomarkers.

Methods	Description	Applications	Reference
PPI	Estimate edge biomarkers according to differential expressed genes and protein-protein interactions.	Classify patients accurately and integrate protein-protein interaction information.	[50]
EdgeBiomarker	Construct an individual specific network through the expression profile of a single sample and integrating the edge and node markers of the biological network.	Diagnose the phenotype of each individual.	[42]
SSN	Construct individual specific networks based on the molecular expression of a single sample.	Clarify the molecular mechanisms of complex diseases of each individual at the system level.	[43]
P-SSN	Construct single-sample network and retain the direct interactions by excluding indirect interactions.	Predict driver mutation genes based on single-sample data, subtype complex diseases and cluster single cells.	[44]
TRFBA	Integrate transcriptional regulatory and metabolic models using a set of expression data for various perturbations.	Integrate transcriptome and metabolome data and improve the quantitatively prediction of growth rate.	[45]
SCS	Use mutation data and expression data to identify personalized driver mutation profiles from the perspective of network controllability.	Predict personalized driver mutation spectrum.	[46]
CSN	Construct a cell-specific network (CSN) for each single cell from scRNA-seq data.	Cluster and pseudo-trajectory at network level; find important non-differential genes.	[47]
c-CSN	Eliminate indirect associations to measure direct associations between genes.	Resolve the direction of differentiation trajectories by quantifying the potency of each single cell.	[48]
NBSBM	Method of sparse Bayesian machine based on network.	Predict drug sensitivity and reveal the underlying mechanism of drug action.	[49]