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Artificial Neural Networks: Methods and Applications / Edition 1

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In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.

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Product Details

  • ISBN-13: 9781617377389
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 10/9/2011
  • Series: Methods in Molecular Biology Series , #458
  • Edition description: Softcover reprint of hardcover 1st ed. 2009
  • Edition number: 1
  • Pages: 254
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.56 (d)

Table of Contents

Chapter 1. Artificial Neural Networks in Biology and Chemistry - the Evolution of a new Analytical Tool

Hugh M. Cartwright

Chapter 2. Overview of Artificial Neural Networks

Jinming Zou, Yi Han, and Sung-Sau So

Chapter 3. Bayesian Regularization of Neural Networks

Frank Burden and Dave Winkler

Chapter 4. Kohonen and Counter-propagation Neural Networks Applied for Mapping and Interpretation of IR Spectra

Marjana Novic

Chapter 5. Artificial Neural Network Modeling in Environmental Toxicology

James Devillers

Chapter 6. Neural Networks in Analytical Chemistry

Mehdi Jalali-Heravi

Chapter 7. Application of Artificial Neural Networks for Decision Support in Medicine

Brendan Larder, Dechao Wang and Andy Revell

Chapter 8. Neural Networks in Building QSAR Models

Igor I. Baskin, Vladimir A. Palyulin, and Nikolai S. Zefirov

Chapter 9. Peptide Bioinformatics- Peptide Classification Using Peptide Machines

Zheng Rong Yang

Chapter 10. Associative Neural Network

Igor V. Tetko

Chapter 11. Neural Networks Predict Protein Structure and Function

Marco Punta and Burkhard Rost

Chapter 12. The Extraction of Information and Knowledge from Trained Neural Networks

David J. Livingstone, Antony Browne, Raymond Crichton, Brian D. Hudson, David Whitley and Martyn G. Ford

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