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Table 2 Comparisons of drug response prediction by QSMART, DNN and statistical methods

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

Cancer type \({\#IC}_{{50}}\) QSMART model Performance \(({R}^{{2}})\)
#All #Drug #Cancer features #Interactions QSMART + (NN/RF/SVM/EN) Compared method
Features Features Residue Others DxM Others NN RF SVM EN \(\hbox {ANOVA}^{{*}}\) \(\hbox {MCA}^{{**}}\)
AG 2971 62 31 0 9 4 18 0.879 0.588 0.581 0.293 0.672 0.656
Bone 3410 84 52 0 13 4 15 0.856 0.621 0.667 0.370 0.693 0.819
Breast 4706 129 70 5 26 12 16 0.880 0.604 0.673 0.496 0.702 0.814
CNS 4250 114 65 0 23 11 15 0.858 0.678 0.719 0.465 0.774 0.851
Cervix 1044 37 29 0 3 1 4 0.864 0.696 0.768 0.455 0.809 0.824
Endometrium 1073 33 21 0 4 4 4 0.878 0.596 0.580 0.328 0.769 0.832
Haematopoietic 4204 119 58 3 24 28 6 0.858 0.615 0.649 0.429 0.679 0.807
Kidney 2458 73 51 0 3 0 19 0.836 0.681 0.734 0.415 0.794 0.820
Large intestine 4628 141 53 10 14 50 14 0.814 0.617 0.692 0.495 0.736 0.794
Liver 1348 48 35 0 4 2 7 0.836 0.646 0.678 0.377 0.730 0.859
Lung (NSCLC) 9205 207 72 7 35 47 46 0.854 0.641 0.707 0.513 0.728 0.819
Lung (others) 7206 162 58 2 16 46 40 0.859 0.602 0.687 0.470 0.725 0.791
Lymphoid 13302 291 72 54 30 86 49 0.873 0.647 0.740 0.495 0.758 0.834
Oesophagus 3337 91 58 0 17 4 12 0.841 0.657 0.699 0.452 0.771 0.838
Ovary 3502 113 64 2 18 9 20 0.844 0.659 0.690 0.522 0.741 0.810
Pancreas 2421 84 60 0 7 0 17 0.833 0.693 0.737 0.492 0.784 0.816
Pleura 1431 36 23 0 5 0 8 0.805 0.629 0.623 0.303 0.776 0.837
Skin 5732 132 64 9 21 15 23 0.875 0.694 0.706 0.458 0.754 0.800
Soft tissue 1938 63 45 0 10 2 6 0.818 0.612 0.671 0.404 0.758 0.786
Stomach 2327 83 49 0 13 16 5 0.836 0.592 0.638 0.392 0.720 0.842
Thyroid 1352 33 25 0 5 0 3 0.830 0.644 0.680 0.398 0.798 0.853
UAT 3856 126 50 1 14 4 57 0.881 0.750 0.758 0.600 0.792 0.841
Urinary tract 1454 68 47 0 5 9 7 0.863 0.645 0.683 0.433 0.754 0.847
Overall 87155   0.863 0.655 0.710 0.460 0.755 0.823
  1. The best performance for each cancer type is highlighted in underlined. The performance of each machine learning method is based on 10-fold cross-validation
  2. \(\hbox {ANOVA}^{*}\), analysis of variance, which did not undergo 10-fold cross-validation. \(\hbox {MCA}^{{**}}\), multiscale convolutional attentive, a drug response prediction method [36]. The performance of MCA is based on its prediction for PKI response (Additional file 2). AG, autonomic ganglia; CNS, central nervous system; DxM, drug–mutation interaction term; EN, elastic net; NN, neural networks; NSCLC, non-small cell lung cancer; \(\hbox {R}^{2}\), coefficient of determination; RF, random forests; SVM, support vector machine; UAT, upper aerodigestive tract; \(\#\hbox {IC}_{50}\), the number of drug responses; #Nodes, the number of nodes in the first and second hidden layers of neural networks