Table 2

Performance of SVM classifiers

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


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.

Xu et al. BMC Systems Biology 2010 4:173   doi:10.1186/1752-0509-4-173

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