Integrating supervised and unsupervised learning in self organizing maps for gene expression data analysis

Authors:
Seferina Mavroudi;Andrei Dragomir;Stergios Papadimitriou;Anastasios Bezerianos
Affiliations:
Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece;Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece;Department of Information Management, Technological Educational Institute, Kavala, Greece;Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

Recently, Self Organizing Maps have been a popular approach to analyze gene expression data. Our paper presents an improved SOM-based algorithm called Supervised Network Self Organizing Map (sNet-SOM), which overcomes the main drawbacks of existing techniques by adaptively determining the number of clusters with a dynamic extension process and integrating unsupervised and supervised learning in an effort to make use of prior knowledge on data. The process is driven by an inhomogeneous measure that balances unsupervised/supervised learning and model complexity criteria. Multiple models are dynamically constructed by the algorithm, each corresponding to an unsupervised/supervised balance, model selection criteria being used to select the optimum one. The design allows us to effectively utilize multiple functional class labeling.