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Table 1 Current drug response prediction approaches

From: Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction

Date Author Best model Compared models Cancer cell line features Drug response Validation Performance
EXP MUT CNV Others GDSC CCLE   
2013/04/30 Menden et al. [10] ANN RF   \(\checkmark\) \(\checkmark\)   \(\checkmark\)   8-fold CV \(\hbox {R}^{2} = 0.72\)
2014/01/01 Jang et al. [11] GLM RF, SVM, PCA, PLS \(\checkmark\) \(\checkmark\) \(\checkmark\) CLS \(\checkmark\) \(\checkmark\) 5-fold CV r = \(\sim\) 0.5
2014/03/03 Geeleher et al. [12] GLM   \(\checkmark\)     \(\checkmark\)   LOOCV AUC = 0.81
2015/06/30 Dong et al. [13] SVM   \(\checkmark\)      \(\checkmark\) 10-fold CV Accuracy = \(\sim\) 0.8
2015/09/29 Zhang et al. [14] Network EN \(\checkmark\)     \(\checkmark\) \(\checkmark\) LOOCV r = 0.6
2016/03/31 Gupta et al. [15] SVM   \(\checkmark\) \(\checkmark\) \(\checkmark\)    \(\checkmark\) LOOCV r = 0.78
2016/09/01 Ammad-Ud-Din et al. [16] Kernel GLM     PWY \(\checkmark\)   5-fold CV \(\rho\) = \(\sim\) 0.22
2016/12/28 Nguyen et al. [17] MANOVA RF \(\checkmark\)     \(\checkmark\)   10-fold CV MCC = 0.18
2017/01/09 Stanfield et al. [18] Network Kernel   \(\checkmark\)   PPI \(\checkmark\) \(\checkmark\) LOOCV AUC = 0.881
2017/07/15 Ammad-Ud-Din et al. [19] GLM RF, SVM, PLS, SGL \(\checkmark\)    PWY \(\checkmark\)   LOOCV \(\rho\) = 0.375
2017/08/28 Geeleher et al. [20] Ridge   \(\checkmark\)     \(\checkmark\)   10-fold CV \(\rho\) = 0.48
2017/09/12 Rahman et al. [21] RF   \(\checkmark\)     \(\checkmark\) \(\checkmark\) 3-fold CV AUC = \(\sim\) 0.3
2018/02/01 Ding et al. [22] DNN EN, SVM \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\) \(\checkmark\) 25-fold CV AUC = 0.87
2018/06/11 Chang et al. [23] CNN RF, SVM     SNP \(\checkmark\)   5% leave-out \(\hbox {R}^{2}\) = 0.843
2018/07/01 Cichonska et al. [24] Kernel   \(\checkmark\)   \(\checkmark\) SNP, MET \(\checkmark\)   10-fold CV r = 0.858
2018/08/15 He et al. [25] Kernel RF, EN, Ridge \(\checkmark\)     \(\checkmark\)   3-fold CV Precision = \(\sim\) 0.35
2018/09/14 Juan-Blanco et al. [26] Network   \(\checkmark\) \(\checkmark\)     \(\checkmark\) LOOCV AUC = \(\sim\) 0.72
2018/09/14 Le and Pham [27] Network Kernel \(\checkmark\) \(\checkmark\)    \(\checkmark\) \(\checkmark\) 5-fold CV r = 0.804
2018/12/07 Liu et al. [28] Network   \(\checkmark\)     \(\checkmark\) \(\checkmark\) 10-fold CV r = 0.73
2019/01/22 Wei et al. [29] Network   \(\checkmark\)     \(\checkmark\) \(\checkmark\) LOOCV r = 0.63
2019/01/31 Wang et al. [30] EN   \(\checkmark\)    PWY   \(\checkmark\) 10-fold CV MSE = \(\sim\) 2.8
2019/01/31 Chiu et al. [31] DNN SVM, PCA, LR \(\checkmark\) \(\checkmark\)    \(\checkmark\)   10% leave-out r = \(\sim\) 0.86
2019/02/27 Li et al. [32] Mixture RF, GLM \(\checkmark\)      \(\checkmark\) 20% leave-out r = 0.882
2019/05/01 Yang et al. [33] Network + SVM Kernel   \(\checkmark\) \(\checkmark\) PPI, MET \(\checkmark\)   5-fold CV AUC = 0.788
2019/07/11 Lind and Anderson [34] RF ANN, SVM   \(\checkmark\)    \(\checkmark\)   5-fold CV r = 0.86
2019/07/29 Liu et al. [35] CNN ANN   \(\checkmark\) \(\checkmark\)   \(\checkmark\)   10% leave-out \(\hbox {R}^{2}\) = 0.826
2019/10/31 Manica et al. [36] MCA + CNN RF, SVM \(\checkmark\)   \(\checkmark\) PPI \(\checkmark\)   5-fold CV \(\underline{\textit{R}^{\textit{2}}\,=\,0.86}\)
2019/11/04 Oskooei et al. [37] Network RF, LR \(\checkmark\)    PPI \(\checkmark\)   30-fold CV r = \(\sim\) 0.9
  1. The best performing method is highlighted in underlined
  2. ANN, artificial neural network; AUC, area under the ROC curve; CCLE, Cancer Cell Line Encyclopedia; CLS, cancer classification; CNN, convolutional neural network; CNV, copy number variation; CV, cross-validation; EN, elastic net; EXP, gene expression; GDSC, Genomics of Drug Sensitivity in Cancer; GLM, generalized linear model, including ridge, elastic net, and lasso regression; DNN, deep neural networks; LOOCV, leave-one-out cross-validation; LR, linear regression; MCA, multiscale convolutional attentive; MCC, Matthews correlation coefficient; MET, methylation; MSE, mean squared error; MUT, gene-level mutation (i.e. whether the gene is mutated or not); PCA, principal component analysis; PLS, partial least squares; PPI, protein–protein interaction; PWY, pathway; r, Pearson correlation coefficient; \(\hbox {R}^{2}\), coefficient of determination; RF, random forests; \(\rho\), Spearman’s rank correlation coefficient; RNN, recurrent neural network; SGL, sparse group lasso; SNP, single nucleotide polymorphism; SVM, support vector machine