Giansalvo Cirrincione (University of Picardie Jules Verne (UPJV), Amiens (France) and University of South Pacific (USP), Suva, Fiji)

Dec 12, 2018 – 11:00 AM

DIISM, Artificial Intelligence laboratory (room 201), Siena SI

Description

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. Biclustering addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The presentation shows 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. Some of the new ideas, which allow the use of GH EXIN to regression problems, are also sketched.