A generic connectionist-based method for on-line feature selection and modelling with a case study of gene expression data analysis

Authors:
N. Kasabov;M. Middlemiss;T. Lane
Affiliations:
Knowledge Engineering and Discovery Research Institute, School of Information Technology, Auckland University of Technology, Auckland, New Zealand;Department of Information Science, University of Otago, PO Box 56, Dunedin, New Zealand;Department of Information Science, University of Otago, PO Box 56, Dunedin, New Zealand
Venue:
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Year:
2003

Citing 2
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Abstract

The paper presents a novel generic method for on-line feature extraction from an incrementally trained connectionist system. The method is applied on a case study problem of identifying genes related to classes of diseases, in particular - 14 types of cancer. The method is based on the evolving connectionist systems ECOS paradigm. The analysis of the discovered features through the application of the proposed method on the case study data, demonstrates the potential of the method for solving important real world problems, such as the problem of defining genes related to diseases.