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Table 2 An overview of computational drug repositioning studies, their adopted strategies, computational approaches, main techniques, data sources, key findings, and evaluation metrics

From: A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

Study Strateg(ies) Computational approach(es) Main technique(s) Data source(s) Evaluation criteria Key finding(s)
Genome Chemical structures Phenome Data mining Machine learning Network analysis
Hu et al. [123] \(\checkmark\) \(\checkmark\)    \(\checkmark\)   CNN KEGG and DrugBank CV, ROC and Comp(Acc, Sen, F1, and AUC) R-TIs
Han et al. [99] \(\checkmark\)   \(\checkmark\) \(\checkmark\) \(\checkmark\)   TM, GCN, and MF OMIM CV and Comp(MAP, AUC, and NDCG) D-GAs
Zeng et al. [106] \(\checkmark\)   \(\checkmark\)   \(\checkmark\)   MM-AE and V-AE DrugBank, repoDB, and CV, AUC, AUC-PR, and Comp(Rec-TK) R-DIs
Yang et al. [127] \(\checkmark\)   \(\checkmark\)    \(\checkmark\) ARM and LP PharmGKB, SIDER, and MedHelp [128] CV, AUC, AUC-PR, and Comp(Send, Spc, and F1) R-DIs
Ozsoy et al. [119] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\)   SM, PDo, and CF PubChem, UniProt, SIDER, and TIRs [35] CV and Comp(Prec, Rec, F1, Spc, and AUC) R-DIs
Segler et al. [124]   \(\checkmark\)    \(\checkmark\)   RNN SMILES EOR R-DIs
Altae-Tran et al. [122]   \(\checkmark\) \(\checkmark\)   \(\checkmark\)   CCN and LSTM SIDER and Tox21 [129] Acc and AUC R-DIs
Papanikolaou et al. [105]   \(\checkmark\)   \(\checkmark\)    NER DrugBank R-RAs
Brown et al. [104]   \(\checkmark\)   \(\checkmark\) \(\checkmark\)   SM and Clust MEDLINE and DrugBank CSs R-DIs
Nugent et al. [59]    \(\checkmark\) \(\checkmark\)    SCM Twitter Comp(Acc) R-DIs
Aliper et al. [121] \(\checkmark\)     \(\checkmark\)   DNN LINCS CV, F1, NNCM, Spr, and Comp(Acc) R-RAs
Lim et al. [118] \(\checkmark\) \(\checkmark\)    \(\checkmark\)   SM and CF ZINC [130] CV and Comp(Acc,and Rec) R-TIs
Sridhar et al. [60] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\)   SM and PSL DrugBank Comp(AUC, AUC-PR, F1) and CSs R-RIs
Zheng et al. [39]   \(\checkmark\)   \(\checkmark\) \(\checkmark\)   SM BindingDB [131] CV, Acc, Spc, Sen, AUC, Prec, F1, Comp(AUC, Sen, and Spc), and CSs R-TIs
Rastegar-Mojarad et al. [103] \(\checkmark\)    \(\checkmark\)   \(\checkmark\) TM and SM SemMedDB [132], UMLS [133], CTD [134], and Medline Comp(CoS) R-DIs
Rakshit et al. [126] \(\checkmark\)    \(\checkmark\)   \(\checkmark\) TM and Genotator [135], PolySearch [95], Pescador [136], and DrugBank Comp(Acc) R-TIs  
Huang et al. [27] \(\checkmark\)     \(\checkmark\) \(\checkmark\) SM and BGL CMap CV, AUC, Comp(AUC), and CSs R-TIs
Zhu et al. [108] \(\checkmark\) \(\checkmark\)   \(\checkmark\)    SM and WOL PharmGKB CSs R-DIs
Tari et al. [102] \(\checkmark\) \(\checkmark\)   \(\checkmark\)    TM and LFRs Medline, GO, UniProt, NCBI, CancerQuest [137], and DrugBank Comp(Rec) and CSs R-DIs
Okada et al. [23] \(\checkmark\) \(\checkmark\)   \(\checkmark\)    GWAS GWASs P and CSs R-DIs
Yang et al. [117] \(\checkmark\) \(\checkmark\)    \(\checkmark\)   CI-PMF DrugBank, BioCarta [138], and CTD [134] Comp(AUC and Prec) and CSs R-DIs
Zhang et al. [116] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\)   SM and BCD DrugBank, NDF-RT [139], and HPRD [140] CV, Comp(AUC), and CSs R-DIs
Bisgin et al. [58]    \(\checkmark\)   \(\checkmark\) \(\checkmark\) SM and LDA SIDER Acc and CSs R-TIs
Tan et al. [38] \(\checkmark\) \(\checkmark\)     \(\checkmark\) SM and Clust PubChem and DrugBank Comp(Acc) R-TIs
Ye et al. [57]    \(\checkmark\)    \(\checkmark\) SM SIDER CV, Acc, and CSs R-TIs
Menden et al. [114] \(\checkmark\) \(\checkmark\)    \(\checkmark\)   FF-PNN and RF CCL [18] CV, Acc, \(R_{p}\), \(R^{2}\), RMSE, and CSs R-TIs
Napolitano et al. [115] \(\checkmark\) \(\checkmark\)    \(\checkmark\)   SVM and CF CMap, NCBI, DrugBank, and ATC [141] CV, Acc, Comp(AUC) R-DIs
Wang et al. [37] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\) \(\checkmark\) SM, KF, and SVM PubChem, KEGG, BRENDA [142], SuperTarget [143], DrugBank, and SIDER CV, Comp(AUC, Acc, Sens, Spc, Prec, and F1), $ CSs R-DIs
Gottlieb et al. [56] \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\)   SM DrugBank,, and SIDER CV, Acc, AUC, and CSs R-TIs
Wu et al. [125] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\) \(\checkmark\) SM and Clust KEGG CV, Comp(Acc), and CSs R-DIs
Chen et al. [107] \(\checkmark\)   \(\checkmark\)   \(\checkmark\) \(\checkmark\) SM and LP Chem2Bio2RDF [144], DrugBank, and Matador [143] Acc, AUC, Copm(Acc and AUC), and CSs R-TIs
Li and Lu [96] \(\checkmark\)    \(\checkmark\)   \(\checkmark\) TM and PharmGKB Acc, Comp(Acc), and CSs PGx
Li and Lu [35] \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\) SM and BGL DrugBank, NDF-RT [139], HPRD [140], and CV (Sen, Spc, and AUC), Comp(Spc, Sen, and AUC), and CSs R-TIs
Hoehndorf et al. [55] \(\checkmark\)   \(\checkmark\)   \(\checkmark\)   SM PhenomeNET [145] and PharmGKB AUC and CSs R-TIs
Gottlieb et al. [113] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\)   SM and LR DrugBank, SMILES, Matador [143], DCDB [146], KEGG, DailyMed [147], SIDER, GO, UniPort, and CV, Acc, P, AUC, and Comp(Acc and AUC) R-DIs
Sirota et al. [14] \(\checkmark\)     \(\checkmark\)   Clust and SM GEO and CMap CSs R-DIs
Hu and Agarwal [13] \(\checkmark\)      \(\checkmark\) SM GEO, CMap, and DrugBank Acc, Comp(Rec and Sens), and CSs R-DIs
Li et al. [101] \(\checkmark\) \(\checkmark\)   \(\checkmark\) \(\checkmark\)   TM, SM and CMap OPHID [148], PubMed CV, Comp(Sen, Spc, PPV, F1, and Acc), and CSs R-DIs
Bleakley et al. [32] \(\checkmark\) \(\checkmark\)    \(\checkmark\) \(\checkmark\) SM, BGL, and SVM KEGG, BRENDA [142], SuperTarget [143], and DrugBank CV, Comp(AYC and AUC-PR), and CSs R-TIs
Kinnings et al. [31] \(\checkmark\) \(\checkmark\)     \(\checkmark\) SM MDDR [149] Comp(Acc) and CSs R-TIs
Keiser et al. [9]   \(\checkmark\)    \(\checkmark\) \(\checkmark\) SM and BGL KEGG, BRENDA [142], SuperTarget [143], and DrugBank CV, Comp(AUC, Sens, Spc, and PPV), and CSs R-TIs
Yamanishi et al. [30] \(\checkmark\) \(\checkmark\)    \(\checkmark\) \(\checkmark\) SM and BGL KEGG, BRENDA [142], SuperTarget [143], and DrugBank CV, Comp(AUC, Sens, Spc, and PPV), and CSs R-TIs
Cheng et al. [95] \(\checkmark\)    \(\checkmark\)   \(\checkmark\) TM PubMed, UniPort, HGMD [150], DrugBank, HMDB [151] Comp(F1) and CSs BER
Campillos et al. [53] \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\)   SM Matador [143], DrugBank, PDSP Ki [152], and STRING [153] Comp(Spc and Sen) and CSs R-DIs
Lamb et al. [12] \(\checkmark\)     \(\checkmark\)   Clust and CMap CHC CSs R-DIs
  1. Acc Accuracy, AE Autoencoder, ARM Association Rules Mining, AUC Area under curve, AUC-PR Area Under The Precision-Recall Curve, BCD Block Coordinate Descent, BER Biological Entity Relationships, BGL Bipartite Graph Learning, CF Collaborative Filtering, CHC Cultured Human Cells, CI-PMF Causal Inference-Probabilistic Matrix Factorization , Clust Clustering, CMap Connectivity Map , CNN Convolutional Neural Network , Comp Comparison With Other Approaches, CoS Co-occurrence Score, CSs Case Studies, CV Cross Validation , D-GAs Disease-Gene Associations, DNN Deep Neural Network, EOR Enrichment Over Random, F1 F1 score/F-measure, FF-PNN Feed-Forward Perceptron Neural Network, GCN Graph Convolutional Network, GWAS Genome-Wide Association Study, KF Kernel Fusion, LDA Latent Dirichlet Allocation, LFRs Logical Facts and Rules, LP Link Prediction, LR Logistic Regression, LSTM Long Short-Term Memory, MAP Mean Average Precision, MF Matrix Factorization, MM-AE Multi-modal Deep Autoencoder, NDCG Normalized Discounted Cumulative Gain, NER Name Entity Recognition, NNCM Neural Net Confusion Matrix, P P-value, PDo Pareto Dominance, PGx Pharmacogenomics, PPV Positive Predictive Value, Prec Precision, PSL Probabilistic Soft Logic, ‘\(R^{2}\)’: Coefficient of Determination , ‘\(R_{p}\)’: Pearson Correlation, R-DIs Drug-Disease Interactions, Rec Recall, Rec-TK Recall @ Top-K, RF Random Forest, RMSE Root Mean Square Error, RNN Recurrent Neural Network, ROC Receiver Operating Characteristic Curve, R-RAs Drug–Drug Associations, R-TIs Drug-Target Interactions, SCM Sample Covariance Matrix, Sen Sensitivity, SM Similarity Measures, Spc Specificity, Spr Separability, SVM Support Vector Machine, TIRs Therapeutic Indication Relationships, TM Text Mining, V-AE Variational Autoencoder, WOL Web Ontology Language