Clustering and artificial neural networks: Classification of variable lengths of Helminth antigens in set of domains
Genet. mol. biol
;
27(4): 673-678, Dec. 2004. ilus, tab, graf
Artigo
em Inglês
| LILACS
| ID: lil-391246
ABSTRACT
A new scheme for representing proteins of different lengths in number of amino acids that can be presented to a fixed number of inputs Artificial Neural Networks (ANNs) speel-out classification is described. K-Means's clustering of the new vectors with subsequent classification was then possible with the dimension reduction technique Principal Component Analysis applied previously. The new representation scheme was applied to a set of 112 antigens sequences from several parasitic helminths, selected in the National Center fo Biotechnology Information and classified into fourth different groups. This bioinformatic tool permitted the establishment of a good correlation with domains that are already well characterized, regardless of the differences between the sequences that were confirmed by the PFAM database. Additionally, sequences were grouped according to their similarity, confirmed by hierarchical clustering using ClustalW.
Texto completo:
DisponíveL
Índice:
LILACS (Américas)
Assunto principal:
Biologia Computacional
/
Antígenos de Helmintos
Tipo de estudo:
Estudo prognóstico
Limite:
Animais
Idioma:
Inglês
Revista:
Genet. mol. biol
Assunto da revista:
Genética
Ano de publicação:
2004
Tipo de documento:
Artigo
País de afiliação:
Brasil
Instituição/País de afiliação:
Universidade Federal de Minas Gerais/BR
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