Table 10

Comparison of accuracy using binary methylation classification.

Methylation classification

Dataset

Methods

Acc [%]

CC


binary

HEP1

EpiGRAPH – SVM linear kernel*

84.90

0.657

binary

HEP1

EpiGRAPH – Decision tree C4.5*

75.80

0.462

binary

HEP1

Matlab – Decision tree**

90.08

0.743

binary

EpiGRAPH 2

EpiGRAPH – SVM linear kernel*

85.20

0.658

binary

EpiGRAPH 2

EpiGRAPH – Decision tree C4.5*

78.60

0.524

binary

EpiGRAPH 2

Matlab – Decision tree**

91.67

0.775

four classes

HEP3

Matlab – Decision tree**

89.39

0.707


*Validation was performed using 10 repetitions of 10 fold cross-validation

** Validation was performed using 10 fold cross-validation

1HEP CGI data (using our attributes and binary methylation classes)

2EpiGRAPH methylation data (using the default EpiGRAPH sequence attributes and binary methylation classes)

3HEP CGI data (using our attributes and four methylation classes)

The average accuracy (Acc) and correlation coefficient (CC) were used to measure fitness.

Previti et al. BMC Bioinformatics 2009 10:116   doi:10.1186/1471-2105-10-116

Open Data