LNCS 2787 Bioinformatics Data Analysis Using an Artificial Immune Network 1st Edition by George Barreto Bezerra, Leandro Nunes de Castro ISBN 3540451927 9783540451921

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ISBN 10: 3540451927

ISBN 13: 9783540451921

Author:  George Barreto Bezerra, Leandro Nunes de Castro 

LNCS 2787 – Bioinformatics Data Analysis Using an Artificial Immune Network 1st Edition: 

This work describes a new proposal for gene expression data clustering based on a combination of an immune network, named aiNet, and the minimal spanning tree (MST). The aiNet is an AIS inspired by the immune network theory. Its main role is to perform data compression and to identify portions of the input space representative of a given data set. The output of aiNet is a set of antibodies that represent the data set in a simplified way. The MST is then built on this network, and clusters are determined by using a new method for detecting the inconsistent edges of the tree. An important advantage of this technique over the classical approaches, like hierarchical clustering, is that there is no need of previous knowledge about the number of clusters and their distributions. The hybrid algorithm was first applied to a benchmark data set to demonstrate its validity, and its results were compared with those produced by other approaches from the literature. Using the full yeast S. cerevisiae gene expression data set, it was possible to detect a strong interconnection of the genes, hindering the perception of inconsistencies that may lead to the separation of data into clusters.

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