Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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 Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Shifting and scaling patterns from gene expression data
Bioinformatics
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
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In recent years, the interest in extracting useful knowledge from gene expression data has experimented an enormous increase with the development of microarray technique. Biclustering is a recent technique that aims at extracting a subset of genes that show a similar behaviour for a subset conditions. It is important, therefore, to measure the quality of a bicluster, and a way to do that would be checking if each data submatrix follows a specific trend, represented by a pattern. In this work, we present an evolutionary algorithm for finding significant shifting patterns which depict the general behaviour within each bicluster. The empirical results we have obtained confirm the quality of our proposal, obtaining very accurate solutions for the biclusters used.