A new model for classifying DNA code inspired by neural networks and FSA

Authors:
Byeong Kang;Andrei Kelarev;Arthur Sale;Ray Williams
Affiliations:
School of Computing, University of Tasmania, Hobart, Tasmania, Australia;School of Computing, University of Tasmania, Hobart, Tasmania, Australia;School of Computing, University of Tasmania, Hobart, Tasmania, Australia;School of Computing, University of Tasmania, Hobart, Tasmania, Australia
Venue:
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Year:
2006

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Abstract

This paper introduces a new model of classifiers CL(V,E,ℓ,r) designed for classifying DNA sequences and combining the flexibility of neural networks and the generality of finite state automata. Our careful and thorough verification demonstrates that the classifiers CL(V,E,ℓ,r) are general enough and will be capable of solving all classification tasks for any given DNA dataset. We develop a minimisation algorithm for these classifiers and include several open questions which could benefit from contributions of various researchers throughout the world.