Elsevier S & T

Neural Networks and Genome Informatics

Wu, C.H.; McLarty, J.W.

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Table of contents
  • Contentsix
  • PART I: Overview1
  • CHAPTER 1. Neural Networks for Genome Informatics3
  • 1.1 What Is Genome Informatics?3
  • 1.2 What Is An Artificial Neural Network?10
  • 1.3 Genome Informatics Applications11
  • 1.4 References12
  • PART II: Neural Network Foundations17
  • CHAPTER 2. Neural Network Basics19
  • 2.1 Introduction to Neural Network Elements19
  • 2.2 Transfer Functions21
  • 2.4 Simple Feed-Forward Network Example25
  • 2.5 Introductory Texts26
  • 2.6 References27
  • CHAPTER 3. Perceptrons and Multilayer Perceptrons29
  • 3.1 Perceptrons29
  • 3.2 Multilayer Perceptrons33
  • 3.3 References38
  • CHAPTER 4. Other Common Architectures41
  • 4.1 Radial Basis Functions41
  • 4.2 Kohonen Self-organizing Maps46
  • 4.4 References50
  • CHAPTER 5. Training of Neural Networks51
  • 5.1 Supervised Learning51
  • 5.3 Unsupervised Learning62
  • 5.4 Software for Training Neural Networks63
  • 5.5 References63
  • PART III: Genome Informatics Applications65
  • CHAPTER 6. Design Issues - Feature Presentation67
  • 6.1 Overview of Design Issues67
  • 6.2 Amino Acid Residues68
  • 6.3 Amino Acid Physicochemical and Structural Features69
  • 6.4 Protein Context Features and Domains71
  • 6.5 Protein Evolutionary Features73
  • 6.6 Feature Representation74
  • 6.7 References76
  • CHAPTER 7. Design Issues - Data Encoding79
  • 7.1 Direct Input Sequence Encoding79
  • 7.2 Indirect Input Sequence Encoding81
  • 7.3 Construction of Input Layer83
  • 7.4 Input Trimming84
  • 7.5 Output Encoding86
  • 7.6 References86
  • CHAPTER 8. Design Issues - Neural Networks89
  • 8.1 Network Architecture89
  • 8.2 Network Learning Algorithm91
  • 8.3 Network Parameters92
  • 8.4 Training and Test Data94
  • 8.5 Evaluation Mechanism97
  • 8.6 References99
  • CHAPTER 9. Applications - Nucleic Acid Sequence Analysis103
  • 9.1 Introduction103
  • 9.2 Coding Region Recognition and Gene Identification105
  • 9.3 Recognition of Transcriptional and Translational Signals107
  • 9.4 Sequence Feature Analysis and Classification110
  • 9.5 References111
  • CHAPTER 10. Applications - Protein Structure Prediction115
  • 10.1 Introduction116
  • 10.2 Protein Secondary Structure Prediction116
  • 10.3 Protein Tertiary Structure Prediction Protein Distance Constraints121
  • 10.4 Protein Folding Qass Prediction123
  • 10.5 References125
  • CHAPTER 11. Applications - Protein Sequence Analysis129
  • 11.1 Introduction129
  • 11.2 Signal Peptide Prediction130
  • 11.3 Other Motif Region and Site Prediction133
  • 11.4 Protein Family Classification136
  • 11.5 References140
  • Part IV : Open Problems and Future Directions143
  • CHAPTER 12. Integration of Statistical Methods into Neural Network Applications145
  • 12.1 Problems in Model Development146
  • 12.2 Training Issues148
  • 12.3 Interpretation of Results149
  • 12.4Further Sources of Information149
  • 12.5 References149
  • CHAPTER 13. Future of Genome Informatics Applications152
  • 13.1 Rule and Feature Extraction from Neural Networks152
  • 13.2 Neural Network Design Using Prior Knowledge156
  • 13.3 Conclusions157
  • 13.4 References158
  • Glossary161
  • Author Index193
  • Subject Index201
Book details
  • Vendor Elsevier S & T
  • SKU 9780080428000
  • ISBN-13 9780080537375
  • Author Wu, C.H.; McLarty, J.W.
  • Category Medical
  • Subject Pharmacology

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Description eBook delivery & access Reviews
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.