Paper Info
Title
Biclustering of Gene Expression Data by Correlation-Based Scatter Search.
Abstract
BACKGROUND: The analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed. Most of them use the Mean Squared Residue as merit function but interesting and relevant patterns from a biological point of view such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of patterns since commonly the genes can present a similar behavior although their expression levels vary in different ranges or magnitudes. METHODS: Scatter Search is an evolutionary technique that is based on the evolution of a small set of solutions which are chosen according to quality and diversity criteria. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method is included in order to select just positively correlated genes. RESULTS: The proposed algorithm has been tested with three real data sets such as Yeast Cell Cycle dataset, human B-cells lymphoma dataset and Yeast Stress dataset, finding a remarkable number of biclusters with shifting and scaling patterns. In addition, the performance of the proposed method and fitness function are compared to that of CC, OPSM, ISA, BiMax, xMotifs and Samba using Gene the Ontology Database.
Year
DOI
Venue
2011
10.1186/1756-0381-4-3
BioData mining
Keywords
Field
DocType
cell cycle,bioinformatics,fitness function,genetics,biomedical research
Data mining,Data analysis,Computer science,Gene expression,Fitness function,Correlation,Gene chip analysis,Biclustering,Merit function,Bioinformatics,Metaheuristic
Journal
Volume
Issue
ISSN
4
1
1756-0381
Citations 
PageRank 
References 
22
0.77
31
Authors
3
Name
Order
Citations
PageRank
Juan A. Nepomuceno1577.10
Alicia Troncoso215320.88
Jesús S. Aguilar-ruiz362559.56