Adaptive modeling and discovery in bioinformatics: The evolving connectionist approach

Authors:
Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1020, New Zealand
Venue:
International Journal of Intelligent Systems - Advantages, Problems, and Trends in Contemporary Intelligent Systems
Year:
2008

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Abstract

Most biological processes that are currently being researched in bioinformatics are complex, dynamic processes that are difficult to model and understand. The paper presents evolving connectionist systems (ECOS) as a general approach to adaptive modeling and knowledge discovery in bioinformatics. This approach extends the traditional machine learning approaches with various adaptive learning and rule extraction procedures. ECOS belong to the class of incremental local learning and knowledge-based neural networks. They are applied here to challenging problems in Bioinformatics, such as: microarray gene expression profiling, gene regulatory network (GRN) modeling, computational neurogenetic modeling. The ECOS models have several advantages when compared to the traditional techniques: fast learning, incremental adaptation to new data, facilitating knowledge discovery through fuzzy rules. © 2008 Wiley Periodicals, Inc.