Table 2

Classification accuracy of the proposed algorithm and alternatives on two subsets of the data in the leave-one-out test.

BRCA1

BRCA2

PROS

PROS-OUT

DP

Others

DP

Others

DP

Others

DP

Others

More correlated

18/18

16.67/18

17/17

16.5/17

59/60

53.3/60

12/15

11.83/15

Less correlated

3/4

1.67/4

4/5

1.67/5

34/41

21.67/41

3/6

0.9/6

DLBCL-FL

ALL-AML

I2000

DP

Others

DP

Others

DP

Others

More correlated

62/62

58.33/62

38/38

34.67/38

58/58

57.83/58

Less correlated

12/15

9.17/15

0/0

0/0

3/4

1.5/4

The first subset includes those test feature vectors that are more correlated to the samples of the correct class (called, more correlated in this table). The second subset consists of those test feature vectors that are more correlated to the samples of the incorrect class (referred to as less correlated). The proposed approach is superior in both subsets, but especially so in the less correlated category. This is achieved by taking advantage of the information encoded in the test sample.

Zhu and Martinez BMC Bioinformatics 2008 9:280   doi:10.1186/1471-2105-9-280