Mining 3D patterns from gene expression temporal data: a new tricluster evaluation measure.

ABSTRACT: Microarrays have revolutionized biotechnological research. The analysis of new data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are applied to create groups of genes that exhibit a similar behavior. Biclustering emerges as a valuable tool for microarray data analysis since it relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. However, if a third dimension appears in the data, triclustering is the appropriate tool for the analysis. This occurs in longitudinal experiments in which the genes are evaluated under conditions at several time points. All clustering, biclustering, and triclustering techniques guide their search for solutions by a measure that evaluates the qua

SUBMITTER: Gutierrez-Aviles D 

PROVIDER: S-EPMC3988738 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature