Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given.

Neural Biclustering in Gene Expression Analysis / Barbiero, P.; Bertotti, A.; Ciravegna, G.; Cirrincione, G.; Piccolo, E.. - ELETTRONICO. - (2017), pp. 1238-1243. (Intervento presentato al convegno 2017 International Conference on Computational Science and Computational Intelligence (CSCI) tenutosi a Las Vegas (USA) nel 14-16 December 2017) [10.1109/CSCI.2017.361].

Neural Biclustering in Gene Expression Analysis

Ciravegna G.;Cirrincione G.;Piccolo E.
2017

Abstract

Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2980666