Chapter 1. Neural Networks for Genome Informatics. 1.1 What Is Genome Informatics?. 1.1.1 Gene Recognition and DNA Sequence Analysis. 1.1.2 Protein Structure Prediction. 1.1.3 Protein Family Classification and Sequence Analysis. 1.2 What Is An Artificial Neural Network?. 1.3 Genome Informatics Applications. 1.4 References.
PART II. Neural Network Foundations. Chapter 2. Neural Network Basics. 2.1 Introduction to Neural Network Elements. 2.1.1 Neurons. 2.1.2 Connections between Elements. 2.2 Transfer Functions. 2.3.1 Summation Operation. 2.3.2 Thresholding Functions. 2.3.3 Other Transfer Functions. 2.4 Simple Feed-Forward Network Example. 2.5 Introductory Texts. 2.6 References. Chapter 3. Perceptrons and Multilayer Perceptrons. 3.1 Perceptrons. 3.1.1 Applications. 3.1.2 Limitations. 3.2 Multilayer Perceptrons. 3.2.1 Applications. 3.2.2 Limitations. 3.3 References. Chapter 4. Other Common Architectures. 4.1 Radial Basis Functions. 4.1.1 Introduction to Radial Basis Functions. 4.1.2 Applications. 4.1.3 Limitations. 4.2 Kohonen Self-organizing Maps. 4.2.1 Background. 4.2.2 Applications. 4.2.3 Limitations. 4.4 References. Chapter 5. Training of Neural Networks. 5.1 Supervised Learning. 5.2.1 Training Perceptrons. 5.2.2 Multilayer Perceptrons. 5.2.3 Radial Basis Functions. 5.2.4 Supervised Training Issues. 5
This book is a comprehensive reference in the field of neural networks and genome informatics. The tutorial of neural network foundations introduces basic neural network technology and terminology. This is followed by an in-depth discussion of special system designs for building neural networks for genome informatics, and broad reviews and evaluations of current state-of-the-art methods in the field. This book concludes with a description of open research problems and future research directions.
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- © Elsevier Science 2000
- 21st September 2000
- Elsevier Science
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