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Tables of Contents for Neural Networks and Genome Informatics
Chapter/Section Title
Page #
Page Count
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