Table 4.

Network based repurposing studies using gene regulatory networks, DTI, Drug–disease, and side-effect association.

	Study name	Method	Datasets	Evaluation criteria
Gene regulatory networks and Gene expression Data
1	DTI prediction for repositioning [113]	Network propagation, scoring based on neighborhood, random walks	GEO repository for Gene expression data	AUC - ROC
2	A network flow approach for repurposing with case study on prostate cancer [115]	Maximum Flow	OMIM, KEGG, PGDB, DrugBank	Precision, Mean, Position
3	NFFinder [119]	Statistical method for analysis	Cmap, DrugMatrix, and GEO	–
4	System biology approach developed by a novel knowledge-driven method [121]	Bayesian network-based approach	Gene-gene interaction	–
5	Functional Module Method with case study of Prostate Cancer [145]	Functional linkage network	TCGA, LINCS, GEO, OMIM	AUC - ROC
6	computational DR using Kolmogorov– Smirnov enrichment testing for [123]	Enrichment (Kolmogorov–Smirnov)	GEO and CTD	–
Protein–protein interaction networks
7	Network Analysis for potential DTI identification [146]	SVM, Logistic regression, L1-regularization, KNN	STRING, DrugBank, Gene cards	Z score and Standard deviation
8	Comprehension of Complex disease using PPIN [112]	–	Gene expression omnibus (GEO)	Harmonic mean and Precision
9	Drug repurposing shared network of PPIs and genes [147]	Similarity	STRING and DrugBank	–
10	PPINs and MMP cellular model [148]	Cross talk by analysis of betweenness centrality	KEGG, OMIM and iRef Index database	–
Drug–target interactions
11	DTI prediction using Probabilistic soft logic [135]	Soft probabilistic logic	DrugBank, KEGG, DCDB, and Matador	AUC, Precision, AUPR
12	Network-based inference for prediction of DTI [136]	–	DrugBank	AUC and Precision
13	Bayesian matrix factorization-based DTI prediction[111]	Prediction based on Bayesian algorithm	DrugBank and DTIs	AUC
14	DTI prediction from integration of chemical and genomic spaces [138]	Bi-partite graph ML	DrugBank and DTIs	AUC
15	Bipartite model for DTI prediction [139]	Supervised method of network inference	DrugBank and DTIs	AUC-ROC, AUPR
Drug–disease and side-effect association
16	Drug repositioning methods based on network inference [149]	–	CTD	AUC-ROC
17	PREDICT, a method to infer the new indications [44]	Logistic regression	DrugBank, SIDER, KEGG (Drug), DCDB, Expression Atlas, OMIM	AUC, precision
18	Clustering of Heterogeneous networks to repurpose the drugs [150]	Clustering	NCBI Gene, KEGG Medicus	–
19	Use of Side effects in Network based approaches to drug repurposing [151]	Statistical analysis	SIDER, FDA approved drugs	–