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\name{promotergene}
\alias{promotergene}
\docType{data}
\title{E. coli promoter gene sequences (DNA)}
\description{
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.
}
\usage{data(promotergene)}
\format{
A data frame with 106 observations and 58 variables.
The first variable \code{Class} is a factor with levels \code{+} for a promoter gene
and \code{-} for a non-promoter gene.
The remaining 57 variables \code{V2 to V58} are factors describing the sequence.
The DNA bases are coded as follows: \code{a} adenine \code{c} cytosine \code{g}
guanine \code{t} thymine
}
\source{
\doi{10.24432/C5S01D}
}
\references{
Towell, G., Shavlik, J. and Noordewier, M. \cr
\emph{Refinement of Approximate Domain Theories by Knowledge-Based
Artificial Neural Networks.} \cr
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
## Create model using Support Vector Machines
promsv <- ksvm(Class~.,data=promotergene,kernel="laplacedot",
kpar="automatic",C=60,cross=4)
promsv
}
\keyword{datasets}
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