Navigating Traditional Chinese Medicine Network Pharmacology and Computational Tools

Table 4

Computational methods/algorithms for network pharmacology.

Type	Method and algorithm	Description	Application^#
Network based	drugCIPHER	A network-based method for drug-target identification based on three linear regression models which integrates drug therapeutic similarity, chemical similarity, and the relevance of targets on PPIs network, respectively [91].	^H[91, 124, 132]
	DMIM	A distance-based mutual information model for indicating the relationship of herbs in TCM formulas [59].	^H[59]
	WNBI	A weight network-based inference method for drug-target prediction by integrating drug similarity and known target similarity [93].	^H[93]
	CIPHER	A computational framework based on a regression model which integrates PPIs, disease phenotype similarities, and gene-phenotype relationships [101].	^D[101]
	LMMA	A reliable approach for constructing disease-related gene network, which combines literature mining and microarray analysis [102].	^D[102]
	ClustEx	A two-step method based on module identification in PPIs network by integrating the time-course microarray data for specific disease-related gene discovery [171].	^D[171]
	MIClique	Identifying disease gene subsets by the combination of mutual information and clique analysis for biological networks [103].	^D[103]
	rcNet	A coupling ridge regression model established based on the known phenotype-gene network for predicting the unknown ones by maximizing the coherence between them [172].	^D[172]
	WSM	A similarity based method for weighted networks matching [104].	^D[104]
	SCAN	A structural clustering algorithm based on biological networks for functional modules discovery [173].	^D[173]
	CIPHER-HIT	A hitting-time-based method for predicting disease genes, which combined the modularity measure into the network inference [53].	^I[53]
	ComCIPHER	An efficient approach for identifying drug-gene-disease comodules underlying the gene closeness data [116].	^I[116]
	PPA	Ping-Pong algorithm: an efficient algorithm for predicting drug-gene associations based on multitypes of data [115].	^I[115]
	ISA	Iterative signature algorithm for searching the modules in heterogeneous network [118].	^I[118]
	NSS	A network stratification strategy to analyze conglomerate networks [174].	^I[174]
Machine learning/others	KNN	K nearest neighbor algorithm: a classical supervised classification algorithm based on closest training samples in the feature space.	^H[81]
	SVM	Support vector machine: a supervised kernel based classification algorithm based on the support vectors which are obtained after the training process by transforming original space into kernel space.	^B[82–84, 96, 97, 175]
	GIP	Gaussian Interaction profile: an efficient classification algorithm for predicting drug-target by constructing a kernel function from the known drug-target interaction profiles [176].	^H[176]
	RF	Random forest: an ensemble learning method for classification based on a multitude of trained decision trees.	^B[82, 83, 177]
	Bayesian classifiers	A popular supervised classification method based on probabilistic graphical model.	^B[85–87, 98, 99]
	SOM	Self-organizing maps: a unsupervised technology based on competition among the output neurons for assignment of the input vectors to map input observations to an output space represented by a grid of output neurons for similarity assessment.	^B[88, 89]
	SEM	Similarity ensemble methods: usually based on several similarity index such as Tanimoto coefficient(Tc) [107, 108] or Jaccard coefficient (Jc) [109].	^B[38, 110, 111]
	PCA	Principal component analysis: a classical data reduction technique for revealing the interrelationship among many variables by creating linear combinations of them into a few new variables to facilitate clustering and model analysis.	^B[100, 124, 178]

Application^#: ^Hherb-related networks construction; ^Ddisease-related networks construction; ^Iintegrative analysis; ^Bboth herb- and- disease-related networks construction.