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E-raamat: Neural Networks and Genome Informatics

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Edited by (University of Texas Health Center at Tyler, Department of Epidemiology and Biomathematics, 11937 ), Edited by (National Biomedical Research Foundation, Georgetown University Medical Center, 3900 Reservoir Road, NW, Washington, DC 20007-2195, USA)
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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.
PART I 1(1)
Overview 1(16)
Chapter 1
3(14)
Neural Networks for Genome Informatics
3(14)
What Is Genome Informatics?
3(1)
Gene Recognition and DNA Sequence Analysis
4(4)
Protein Structure Prediction
8(1)
Protein Family Classification and Sequence Analysis
9(1)
What Is An Artificial Neural Network?
10(1)
Genome Informatics Applications
11(1)
References
12(5)
PART II 17(1)
Neural Network Foundations 17(48)
Chapter 2
19(10)
Neural Network Basics
19(10)
Introduction to Neural Network Elements
19(1)
Neurons
19(1)
Connections between Elements
20(1)
Transfer Functions
21(1)
Summation Operation
21(1)
Thresholding Functions
22(2)
Other Transfer Functions
24(1)
Simple Feed-Forward Network Example
25(1)
Introductory Texts
26(1)
References
27(2)
Chapter 3
29(12)
Perceptrons and Multilayer Perceptrons
29(12)
Perceptrons
29(1)
Applications
29(4)
Limitations
33(1)
Multilayer Perceptrons
33(3)
Applications
36(2)
Limitations
38(1)
References
38(3)
Chapter 4
41(10)
Other Common Architectures
41(10)
Radial Basis Functions
41(1)
Introduction to Radial Basis Functions
41(3)
Applications
44(2)
Limitaions
46(1)
Kohonen Self-organizing Maps
46(1)
Background
47(1)
Applications
48(2)
Limitations
50(1)
References
50(1)
Chapter 5
51(14)
Training of Neural Networks
51(14)
Supervised Learning
51(1)
Training Perceptrons
52(3)
Multilayer Perceptrons
55(3)
Radial Basis Functions
58(1)
Supervised Training Issues
59(3)
Unsupervised Learning
62(1)
Software for Training Neural Networks
63(1)
References
63(2)
PART III 65(1)
Genome Informatics Applications 65(78)
Chapter 6
67(12)
Design Issues-Feature Presentation
67(12)
Overview of Design Issues
67(1)
Amino Acid Residues
68(1)
Amino Acid Physicochemical and Structural Features
69(2)
Protein Context Features and Domains
71(2)
Protein Evolutionary Features
73(1)
Feature Representation
74(2)
References
76(3)
Chapter 7
79(10)
Design Issues-Data Encoding
79(10)
Direct Input Sequence Encoding
79(2)
Indirect Input Sequence Encoding
81(2)
Construction of Input Layer
83(1)
Input Trimming
84(2)
Output Encoding
86(1)
References
86(3)
Chapter 8
89(14)
Design Issues-Neural Networks
89(14)
Network Architecture
89(2)
Network Learning Algorithm
91(1)
Network Parameters
92(2)
Training and Test Data
94(1)
Network Generalization
94(1)
Data Quality and Quantity
95(1)
Benchmarking Data Set
96(1)
Evaluation Mechanism
97(2)
References
99(4)
Chapter 9
103(12)
Applications-Nucleic Acid Sequence Analysis
103(12)
Introduction
103(2)
Coding Region Recognition and Gene Identification
105(2)
Recognition of Transcriptional and Translational Signals
107(3)
Sequence Feature Analysis and Classification
110(1)
References
111(4)
Chapter 10
115(14)
Applications-Protein Structure Prediction
115(14)
Introduction
116(1)
Protein Secondary Structure Prediction
116(5)
Protein Tertiary Structure Prediction
121(2)
Protein Distance Constraints
123(1)
Protein Folding Class Prediction
123(2)
References
125(4)
Chapter 11
129(14)
Applications-Protein Sequence Analysis
129(14)
Introduction
129(1)
Signal Peptide Prediction
130(3)
Other Motif Region and Site Prediction
133(3)
Protein Family Classification
136(4)
References
140(3)
Part IV 143(1)
Open Problems and Future Directions 143(18)
Chapter 12
145(7)
Integration of Statistical Methods into Neural Network Applications
145(7)
Problems in Model Development
146(1)
Input Variable Selection
146(1)
Number of Hidden Layers and Units
147(1)
Comparison of Architectures
147(1)
Need for Benchmark Data
148(1)
Training Issues
148(1)
Interpretation of Results
149(1)
Further Sources of Information
149(1)
References
149(3)
Chapter 13
152(9)
Future of Genome Informatics Applications
152(9)
Rule and Feature Extraction from Neural Networks
152(1)
Rule Extraction from Pruned Networks
152(1)
Feature Extraction by Measuring Importance of Inputs
153(1)
Feature Extraction Based on Variable Selection
154(1)
Network Understanding Based on Output Interpretation
155(1)
Neural Network Design Using Prior Knowledge
156(1)
Conclusions
157(1)
References
158(3)
Glossary 161(32)
Author Index 193(8)
Subject Index 201


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