Methods to bicluster validation and comparison in microarray data

Authors:
Rodrigo Santamaría;Luis Quintales;Roberto Therón
Affiliations:
University of Salamanca;University of Salamanca;University of Salamanca
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 5
Cited 2

Algorithms for clustering data

Algorithms for clustering data
On Clustering Validation Techniques

Journal of Intelligent Information Systems
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering Models for Structured Microarray Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A systematic comparison and evaluation of biclustering methods for gene expression data

Bioinformatics

Stability-based validation of bicluster solutions

Pattern Recognition
Ensemble methods for biclustering tasks

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are lots of validation indexes and techniques to study clustering results. Biclustering algorithms have been applied in Systems Biology, principally in DNA Microarray analysis, for the last years, with great success. Nowadays, there is a big set of biclustering algorithms each one based in different concepts, but there are few intercomparisons that measure their performance. We review and present here some numerical measures, new and evolved from traditional clustering validation techniques, to allow comparisons and validation of biclustering algorithms.