Artificial Neural Networks. Methods and Applications (Methods in Molecular Biology, Vol. 458) (Softcover)

ABOUT THIS BOOK

As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of 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. In the tradition of the highly successful Methods in Molecular Biology™ series, this volume exhibits clear, easy-to-use information with many step-by-step laboratory protocols.

Comprehensive and state-of-the-art, Artificial Neural Networks is an excellent guide to this accelerating technological field of study.

Content Level » Professional/practitioner
Keywords » ANN - Artificial intelligence - Network architecture
Related subjects » Artificial Intelligence - Computer Science - Neuroscience

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