Paper Info
Title
Evolutionary biclustering based on expression patterns
Abstract
The majority of the biclustering approaches for microarray data analysis use the Mean Squared Residue (MSR) as the main evaluation measure for guiding the heuristic. MSR has been proven to be inefficient to recognize several kind of interesting patterns for biclusters. Transposed Virtual Error (VEt) has recently been discovered to overcome MSR drawbacks, being able to recognize shifting and/or scaling patterns. In this work we propose a parallel evolutionary biclustering algorithm which uses VEt as the main part of the fitness function, which has been designed using the volume and overlapping as other objectives to optimize. The resulting algorithm has been tested on both synthetic and benchmark real data producing satisfactory results. These results has been compared to those of the most popular biclustering algorithm developed by Cheng and Church and based in the use of MSR.
Year
DOI
Venue
2011
10.1109/ISDA.2011.6121711
Intelligent Systems Design and Applications
Keywords
Field
DocType
data analysis,evolutionary computation,mean square error methods,pattern clustering,MSR drawback,benchmark real data,expression pattern,fitness function,mean squared residue,microarray data analysis,parallel evolutionary biclustering algorithm,scaling pattern,synthetic data,transposed virtual error,biclustering,gene expression data,genetic algorithm,microarray analysis,parallel computing
Data mining,Heuristic,Pattern clustering,Computer science,Evolutionary computation,Fitness function,Artificial intelligence,Biclustering,Machine learning,Genetic algorithm
Conference
ISSN
ISBN
Citations 
2164-7143
978-1-4577-1676-8
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
Beatriz Pontes100.34
Raúl Giráldez200.34
Aguilar-Ruiz, J.S.3323.04