
Type  Method and algorithm  Description  Application^{#} 

Network based  drugCIPHER  A networkbased method for drugtarget 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 distancebased mutual information model for indicating the relationship of herbs in TCM formulas [59].  ^{
H}[59] 
WNBI  A weight networkbased inference method for drugtarget 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 genephenotype relationships [101].  ^{
D}[101] 
LMMA  A reliable approach for constructing diseaserelated gene network, which combines literature mining and microarray analysis [102].  ^{
D}[102] 
ClustEx  A twostep method based on module identification in PPIs network by integrating the timecourse microarray data for specific diseaserelated 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 phenotypegene 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] 
CIPHERHIT  A hittingtimebased method for predicting disease genes, which combined the modularity measure into the network inference [53].  ^{
I}[53] 
ComCIPHER  An efficient approach for identifying druggenedisease comodules underlying the gene closeness data [116].  ^{
I}[116] 
PPA  PingPong algorithm: an efficient algorithm for predicting druggene 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 drugtarget by constructing a kernel function from the known drugtarget 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  Selforganizing 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] 
