Interpreting gene profiles from biomedical literature mining with self organizing maps

Authors:
Shi Yu;Steven Van Vooren;Bert Coessens;Bart De Moor
Affiliations:
Department of Electrical Engineering, University of Leuven, Belgium;Department of Electrical Engineering, University of Leuven, Belgium;Department of Electrical Engineering, University of Leuven, Belgium;Department of Electrical Engineering, University of Leuven, Belgium
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization

Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Self organization of a massive document collection

IEEE Transactions on Neural Networks

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Abstract

We present an approach to interpret gene profiles derived from biomedical literature using Self Organizing Maps (SOMs). Comparison of different clustering algorithms shows that SOMs perform better in grouping high dimensional gene profiles when a lot of noise is present in the data. Qualitative analysis of the clustering results prove that SOMs allow an in-depth interpretation of gene profiles with biological relevance.