Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/94260
Type: Artigo de periódico
Title: A Hierarchical Immune Network Applied To Gene Expression Data
Author: Bezerra G.B.
De Castro L.N.
Von Zuben F.J.
Abstract: This paper describes a new proposal for gene expression data analysis. The method used is based on a hierarchical approach to a hybrid algorithm, which is composed of an artificial immune system, named aiNet, and a well known graph theoretic tool, the minimal spanning tree (MST). This algorithm has already proved to be efficient for clustering gene expression data, but its performance may decrease in some specific cases. However, through the use of a hierarchical approach of immune networks it is possible to improve the clustering capability of the hybrid algorithm, such that it becomes more efficient, even when the data set is complex. The proposed methodology is applied to the yeast data and gives important conclusions of the similarity relationships among genes within the data set. © Springer-Verlag 2004.
Editor: 
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-35048861447&partnerID=40&md5=00e2e7ff52da010337023de3dec92eea
Date Issue: 2004
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.