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.
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Bezerra, G.B., de Castro, L.N., Von Zuben, F.J. (2004). A Hierarchical Immune Network Applied to Gene Expression Data. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_2
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DOI: https://doi.org/10.1007/978-3-540-30220-9_2
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