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Hidden Markov models and neural networks

Part 4. Bioinformatics

4.8. Modern Programming Paradigms in Biology

Specialist Review

  1. Stefan C. Kremer1,
  2. Pierre Baldi2

Published Online: 15 JAN 2005

DOI: 10.1002/047001153X.g409201

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Kremer, S. C. and Baldi, P. 2005. Hidden Markov models and neural networks. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.8:98.

Author Information

  1. 1

    University of Guelph, Guelph, ON, Canada

  2. 2

    University of California, Irvine, CA, USA

Publication History

  1. Published Online: 15 JAN 2005

Abstract

In this contribution, we discuss the application of machine learning methods, particularly hidden Markov models and artificial neural networks, to bioinformatics tasks. We begin with a brief summary of the two paradigms as computational and learning systems and describe the nature of the problems to which these two approaches can be applied. Then, we give some examples of their application to bioinformatics, briefly discuss the strengths and weaknesses of these two approaches, and provide some avenues for further reading.

Keywords:

  • hidden Markov models;
  • neural networks;
  • machine learning