Recent advances in gene expression data clustering: a case study with comparative results
Genet. mol. res. (Online)
; Genet. mol. res. (Online);4(3): 514-524, 2005. ilus, graf
Article
em En
| LILACS
| ID: lil-444960
Biblioteca responsável:
BR1.1
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
LILACS
Assunto principal:
Redes Neurais de Computação
/
Modelos Imunológicos
/
Biologia Computacional
/
Perfilação da Expressão Gênica
Idioma:
En
Revista:
Genet. mol. res. (Online)
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2005
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Brasil