E. coli promoter gene sequences (DNA)

Promoters have a region where a protein (RNA polymerase) must make contact and the helical DNA sequence must have a valid conformation so that the two pieces of the contact region spatially align. The data contains DNA sequences of promoters and non-promoters.

data(promotergene)

Format

A data frame with 106 observations and 58 variables. The first variable Class is a factor with levels + for a promoter gene and - for a non-promoter gene. The remaining 57 variables V2 to V58 are factors describing the sequence. The DNA bases are coded as follows: a adenine c cytosine g guanine t thymine

References

Towell, G., Shavlik, J. and Noordewier, M.
Refinement of Approximate Domain Theories by Knowledge-Based Artificial Neural Networks.
In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90)

Examples

data(promotergene)

## Create classification model using Gaussian Processes

prom <- gausspr(Class~.,data=promotergene,kernel="rbfdot",
                kpar=list(sigma=0.02),cross=4)
prom
#> Gaussian Processes object of class "gausspr" 
#> Problem type: classification 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.02 
#> 
#> Number of training instances learned : 106 
#> Train error : 0 
#> Cross validation error : 0.1695157 

## Create model using Support Vector Machines

promsv <- ksvm(Class~.,data=promotergene,kernel="laplacedot",
               kpar="automatic",C=60,cross=4)
promsv
#> Support Vector Machine object of class "ksvm" 
#> 
#> SV type: C-svc  (classification) 
#>  parameter : cost C = 60 
#> 
#> Laplace kernel function. 
#>  Hyperparameter : sigma =  0.0160396542958821 
#> 
#> Number of Support Vectors : 102 
#> 
#> Objective Function Value : -285.7855 
#> Training error : 0.018868 
#> Cross validation error : 0.086182