Zhu et al., 2009
|
DrugBank |
BioGRID |
Connectivity degree, cluster coefficient, distance-based measures, topological coefficient |
Support Vector Machine |
AUC |
AUC: 69.21% |
Jeon et al., 2014
|
DrugBank, Therapeutics Target Database |
Bossi and Lehner, 2009
|
GARP score, RMA intensity, row chromosomal copy number, mutation occurrence and closeness centrality (combined or isolated) |
SVM-recursive feature elimination (SVM-REF) method for feature selection; SVM-RBF kernels for predictions |
Accuracy, Specificity, AUC |
Avg. accuracy: 91.69% Avg. specificity: 91.91% Avg. AUC: 78% (combined) |
Li et al., 2015
|
DrugBank |
HIPPIE |
Combination of various network distance-based measures and sequence features of proteins |
Random Forest with minimum Redundancy Maximum Relevance (mRMR) Feature Selection |
Accuracy, Sensitivity, Specificity, Precision, Matthews correlation coefficient |
Accuracy: 87.05% Sensitivity: 90.28% Specificity: 83.83% Precision: 84.82% Matthews correlation coefficient: 0.7427 (Avg. of 10 random samples) |
Laenen et al., 2013
|
PubChem, ChEMBL and BindingDB |
STRING, GEO (Edgar et al., 2002) |
Combination of kernel and correlation diffusion and differential gene expression |
Rank-based method |
AUC |
Kernel: 76–91% Correlation: 89–92% |
Emig et al., 2013
|
Integrity |
metaBase (Bureeva et al., 2009), GEO (Edgar et al., 2002) |
Combination of neighborhood scoring, interconnectivity, network propagation, random walk and differential gene expression |
Logistic regression model |
AUC |
AUC: 63.27–93.19% |
Yao and Rzhetsky, 2008
|
DrugBank |
HPRD |
Combination of connectivity, betweenness, tissue expression entropy, constant corrected ratio of non-synonymous and synonymous mutations and functional family assignment |
Naive Bayesian, logistic regression, radial basis function network, Bayesian networks |
AUC |
Naive Bayes: 70.43% Logistic regression: 72.57% RBF network: 60.93% Bayesian Network: 72.31% |
Costa et al., 2010
|
Yildirim et al., 2007
|
BioGRID, DIP, HPRD, IntAct, MINT, MIPS-MPPI, TRED, human metabolic model Recon 1 |
Combination of several network measures, tissue expression profile and subcellular localization |
Decision tree-based meta-classifier |
AUC, Recall, Precision |
AUC: 82% Recall: 78.2% Precision: 74.8% |