Virtual error: a new measure for evolutionary biclustering

Authors:
Beatriz Pontes;Federico Divina;Raúl Giráldez;Jesús S. Aguilar-Ruiz
Affiliations:
Department of Computer Science, University of Seville, Sevilla, Spain;School of Engineering, Pablo de Olavide University, Sevilla, Spain;School of Engineering, Pablo de Olavide University, Sevilla, Spain;School of Engineering, Pablo de Olavide University, Sevilla, Spain
Venue:
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2007

Citing 6
Cited 5

Clustering by pattern similarity in large data sets

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation

IEEE Transactions on Knowledge and Data Engineering
Shifting and scaling patterns from gene expression data

Bioinformatics
A systematic comparison and evaluation of biclustering methods for gene expression data

Bioinformatics

Evolutionary metaheuristic for biclustering based on linear correlations among genes

Proceedings of the 2010 ACM Symposium on Applied Computing
Iterated local search for biclustering of microarray data

PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Measuring the quality of shifting and scaling patterns in biclusters

PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data

Knowledge-Based Systems
Predicting missing values with biclustering: A coherence-based approach

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many heuristics used for finding biclusters in microarray data use the mean squared residue as a way of evaluating the quality of biclusters. This has led to the discovery of interesting biclusters. Recently it has been proven that the mean squared residue may fail to identify some interesting biclusters. This motivates us to introduce a new measure, called Virtual Error, for assessing the quality of biclusters in microarray data. In order to test the validity of the proposed measure, we include it within an evolutionary algorithm. Experimental results show that the use of this novel measure is effective for finding interesting biclusters, which could not have been discovered with the use of the mean squared residue.