Table 2.

Examples of dynamic network analysis methods

Classification of methods	Characteristics of methods	Data sources	Number of modules	Strengths and weaknesses	Network type(s)a	Software	References
Dynamic Bayesian Networks (DBNs)	DBN inference algorithm: searching for high-scoring networks that describe probabilistic relationships between discrete variables. (1) Bayesian scoring metrics; (2) search heuristics; (3) influence score	Gene expression data	/	Advantages: to infer cyclic phenomena such as feedback loops; infer direction of causality because they incorporate temporal information. Limitation: imprecision	/	/	[83]
Network component analysis (NCA)	NCA is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of transcription factors on each target gene	Gene expression data + regulatory network	/	Limitations: difficult to predict the direction of transcription factor activity; inability to incorporate time course information from the data set	Transcriptional regulatory networks	/	[85]
State space model	State space model automatically identifies the temporal aggregations of the gene expression profiles and assembles them into large scale gene networks	Time-course microarray gene expression profiles	/	The state space model has the potential to infer large-scale gene networks, but its applicability is limited by when the length of time series is exceedingly short, e.g., <10	Gene networks	TRANS-MNET	[87]

"/" Denotes that the contents were not found in the literature

^aThe network type is the primary network where the method has been tested in the literature, but many methods are also applicable to other types of molecular networks