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Table 2 Performance of SVM classifiers

From: SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

Datatype_kernel TP FP TN FN TPR FPR Accuracy
micro_linear 42 17 53 4 0.91 0.24 0.82
micro_poly 39 24 46 7 0.85 0.34 0.73
micro_RBF 37 3 67 9 0.80 0.04 0.90
chip_binary_linear 35 10 60 11 0.78 0.13 0.84
chip_binary_poly 36 5 65 10 0.78 0.07 0.87
chip_binary_RBF 39 8 62 7 0.85 0.11 0.87
chip_contin_linear 38 7 63 8 0.83 0.10 0.87
chip_contin_poly 36 8 62 10 0.78 0.11 0.84
chip_contin_RBF 39 5 65 7 0.85 0.07 0.90
weight_binary_linear 39 9 61 7 0.85 0.13 0.86
weight_binary_poly 37 5 65 9 0.80 0.07 0.88
weight_binary_RBF 40 4 66 6 0.87 0.06 0.91
weight_contin_linear 41 9 61 5 0.89 0.13 0.88
weight_contin_poly 37 8 62 9 0.80 0.11 0.85
weight_contin_RBF 42 5 65 4 0.91 0.07 0.92
simple_binary_linear 39 9 61 7 0.85 0.13 0.86
simple_binary_poly 37 3 67 9 0.80 0.04 0.90
simple_binary_RBF 42 3 67 4 0.91 0.04 0.94
simple_contin_linear 41 9 61 5 0.89 0.13 0.88
simple_contin_poly 43 17 53 3 0.93 0.24 0.83
simple_contin_RBF 41 3 67 5 0.89 0.04 0.93
  1. Comparison of performance of several kernel functions used for SVM learning applied on single and heterogeneous data types (mRNA expression and ChIP-seq). The best performer for each category is bold-highlighted. Kernel functions include: linear kernel, polynomial kernel (poly) and Gaussian radial basis kernel (RBF) (see methods). Datasets include: micro-mRNA expression microarrays; chip_binary-ChIP-seq data with pre-processing into binary feature values; chip_contin-ChIP-seq data with pre-processing into continuous feature values. Performance of two data integration strategies: "weight"- weighted kernel matrices; "simple"- one kernel matrix by concatenation of the two data types (see methods). As an example, "simple_binary_poly" means the approach of concatenating microarray and binary ChIP-seq data and training using an SVM with a polynomial kernel function.