promotergene: E. coli promoter gene sequences (DNA)

In elad663/kernlab: Kernel-Based Machine Learning Lab

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 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

Source

UCI Machine Learning data repository
ftp://ftp.ics.uci.edu/pub/machine-learning-databases/molecular-biology/promoter-gene-sequences

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

## Create model using Support Vector Machines

promsv <- ksvm(Class~.,data=promotergene,kernel="laplacedot",
               kpar="automatic",C=60,cross=4)
promsv

elad663/kernlab documentation built on May 7, 2019, 6:06 a.m.