Libro de Resúmenes / Book of Abstracts (Español/English)

Resumenes 151
DNA microarrays are typical examples of high dimensionality context.
They are part of a new class of biotechnologies which allow the monitoring
(and quantification) of expression levels for thousands of genes
simultaneously – gene expression profiles.
DNA microarrays data contain more variables than observations (few
profiles that quantify the expression levels of several thousands of genes).
For this reason, traditional classification multivariate methods tend to
produce unstable results due to overparametrization. In this context, the
feature selection acquires relevance.
In supervised classification, all the definitions for “feature selection”
emphasize the problem of choosing a minimum subset of relevant features
to characterize and discriminate between classes in datasets different from
the learning set.
We analyze a data set from an experiment with DNA microarrays
comparing wild type (Col-0) and mutant plants (Npr-1, Jar-1) of Arabidopsis
thaliana. There were evaluated 11250 gene expression profiles, previously
and 18 hours after the inoculation with the fungus Erysiphe cichoracearum.
The goal is to choose a manageable set of genes composed by those
which are clearly activated or repressed by the infection, and by those
which distinguish among the different kinds of plants.
Obtain pertinent feature subsets is very important for the
classification task because the experimental confirmation of the statistic
discoveries only can be done with a small set of genes by costly laboratory
techniques.
An empirical comparison of the performance of different feature
selection methods with the dataset presented is carried out in this work.
The methods presented here are based on distance and consistency
measures and built from known procedures such as the filter RELIEF (Kira
and Rendel, 1992).