Recent advances in gene expression data clustering: a case study with comparative results.
Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.
Bezerra
George B
GB
Laboratório de Bioinformática e Computação Bio-Inspirada (LBiC/DCA/FEEC), UNICAMP, Caixa Postal 6101, 13083-852 Campinas, SP, Brazil. bezerra@dca.fee.unicamp.br
Cançado
Geraldo M A
GM
Menossi
Marcelo
M
Castro
Leandro N de
LN
Von Zuben
Fernando J
FJ
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D018448:Models, Immunological; D016571:Neural Networks, Computer