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Table 6 Results of experiment 2: prioritization of prostate cancer genes by genomic data fusion

From: L2-norm multiple kernel learning and its application to biomedical data fusion

Name

Ensemble id

References

L ∞

L∞(0.5)

L 1

L 2

Endeavour

CPNE

ENSG00000085719

Thomas et al.

0.3030

0.2323

0.1010

0.1212

-

   

31/100

24/100

11/100

13/100

70/100

CDH23

ENSG00000107736

Thomas et al.

0.0606

0.0303

0.0202

0.0101

-

   

7/100

4/100

3/100

2/100

78/100

EHBP1

ENSG00000115504

Gudmundsson et al.

0.5354

0.5152

0.3434

0.3939

-

   

54/100

52/100

35/100

40/100

57/100

MSMB

ENSG00000138294

Eeles et al.

0.0202

0.0202

0.0505

0.0303

-

  

Thomas et al.

3/100

3/100

6/100

4/100

69/100

KLK3

ENSG00000142515

Eeles et al.

0.3434

0.3535

0.2929

0.2929

-

   

35/100

36/100

30/100

30/100

28/100

JAZF1

ENSG00000153814

Thomas et al.

0.0505

0.0202

0.0202

0.0202

-

   

6/100

3/100

3/100

3/100

7/100

LMTK2

ENSG00000164715

Eeles et al.

0.3131

0.4646

0.8081

0.7677

-

   

32/100

47/100

81/100

77/100

31/100

IL16

ENSG00000172349

Thomas et al.

0

0.0101

0.0303

0.0101

-

   

1/100

2/100

4/100

2/100

72/100

CTBP2

ENSG00000175029

Thomas et al.

0.8283

0.5758

0.6364

0.6869

-

   

83/100

58/100

64/100

69/100

38/100

  1. Results of experiment 2: prioritization of prostate cancer genes by genomic data fusion. For each novel prostate cancer gene, the first row shows the error of AUC values and the second row lists the ranking position of the prostate cancer gene among its 99 closet neighboring genes.