Published June 13, 2016
| Version v1
Publication
Revisiting the Yeast Cell Cycle Problem with the Improved TriGen Algorithm
Description
Analyzing microarray data represents a computational
challenge due to the characteristics of these data.
Clustering techniques are widely applied to create groups of
genes that exhibit a similar behavior under the conditions
tested. Biclustering emerges as an improvement of classical
clustering since it relaxes the constraints for grouping allowing
genes to be evaluated only under a subset of the conditions
and not under all of them. However, this technique is not
appropriate for the analysis of temporal microarray data in
which the genes are evaluated under certain conditions at
several time points. On a previous work we presented the
TriGen algorithm, a genetic algorithm that finds triclusters
of gene expression that take into account the experimental
conditions and the time points simultaneously, and was applied
to the yeast (Saccharomyces Cerevisiae) cell cycle problem.
In this article we present some improvements on the genetic
algorithm and we also present the results of applying the
improved TriGen algorithm to the yeast cell cycle problem,
where the goal is to identify all genes whose expression levels
are regulated by the cell cycle.
Additional details
Identifiers
- URL
- https://idus.us.es/handle/11441/42159
- URN
- urn:oai:idus.us.es:11441/42159