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The Scientific World Journal - Volume 2014 2014, Article ID 624371, 16 pages -

Research ArticleDepartment of Computer Science, University of Seville, Avenida Reina Mercedes s-n, 41012 Seville, Spain

Received 28 December 2013; Accepted 26 February 2014; Published 31 March 2014

Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang

Copyright © 2014 David Gutiérrez-Avilés and Cristina Rubio-Escudero. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 quality of clusters. We present an evaluation measure for triclusters called Mean Square Residue 3D. This measure is based on the classic biclustering measure Mean Square Residue. Mean Square Residue 3D has been applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related to their functional annotations extracted from the Gene Ontology project.

Author: David Gutiérrez-Avilés and Cristina Rubio-Escudero



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