Abstract
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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References
Baldi, P., Brunak, S.: Bioinformatics - The Machine Learning Approach, 2nd edn. MIT Press, Cambridge (2001)
Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press, Cambridge (1959)
de Castro, L.N., Von Zuben, F.J.: aiNet: An artificial Immune Network for Data Analysis. In: Abbass, H.A., Saker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, Ch. XII, pp. 231–259. Idea Group Publishing, USA (2001)
de Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications. In: GECCO 2000 Proc. of the Genetic and Evolutionary Computation Conference – Workshop Proceedings, pp. 36–37 (2000)
de Castro, L.N., Von Zuben, F.J.: An Evolutionary Immune Network for Data Clustering. In: Proc. of IEEE SBRN – Brazilian Symposium on Neural Networks, pp. 84–89 (2000b)
Eisen, M.B., Spellman, P.T., Brow, P.O., Botstein, D.: Cluster Analysis and Display of Genome-wide Expression Patterns. Proc. Natl. Acad. Sci. 95, 14863–14868 (1998)
Everitt, B.: Cluster Analysis. Heinemann Educational Books (1993)
Fausset, L.: Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Prentice-Hall, New Jersey (1994)
Gomes, L.C.T., Von Zuben, F.J., e Moscato, P.: Ordering Gene Expression Data Using One-Dimensional Self-Organizing Maps. In: Proc. of the 1st Brazilian Workshop on Bioinformatics, Gramado, RS, Brazil, pp. 91–93 (2002)
Herwig, R., Poustka, A.J., Mller, C., Bull, C., Lehrach, H., O’Brien, J.: Largescale clustering of cDNA-fingerprinting data. Genome Res. 9, 1093–1105 (1999)
Jerne, N.K.: Towards a Network Theory of the Immune System. Ann. Immunol (Inst. Pasteur), 373–389 (1974)
Lockhart, D.J., et al.: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology 14, 1675–1680 (1996)
Luscombe, N.M., Greenbaum, D., Gerstein, M.: What is Bioinformatics? – A Proposed Definition and Overview of the Field. Methods of Information in Medicine 40, 346–358 (2001)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Nova York (1999)
Prim, R.C.: Shortest Connection Networks and Some Generalizations”, Bell Sys. Tech. Journal 36, 1389–1401 (1957)
Schena, M., et al.: Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl. Acad. Sci. USA 93, 10614–10619 (1996)
Xu, Y., Olman, V., Dong, X.: Minimum Spanning Trees for Gene Expression Data Clustering. Bioinformatics 18, 536–545 (2002)
Yeung, K.Y.: Cluster Analysis of Gene Expression Data., Ph.D. Thesis, Computer Science, University of Washington, Seattle, WA, USA (2001)
Zahn, C.T.: Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters. IEEE Trans. on Computers C-20(1), 68–86 (1971)
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Bezerra, G.B., de Castro, L.N. (2003). Bioinformatics Data Analysis Using an Artificial Immune Network. In: Timmis, J., Bentley, P.J., Hart, E. (eds) Artificial Immune Systems. ICARIS 2003. Lecture Notes in Computer Science, vol 2787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45192-1_3
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DOI: https://doi.org/10.1007/978-3-540-45192-1_3
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