Neural networks and genome informatics / C.H. Wu, J.W. McLarty.

Format:

Book

Author/Creator:

Wu, Cathy H.

Contributor:

McLarty, J. W. (Jerry Wayne)

Series:

Methods in computational biology and biochemistry ; v. 1.

Methods in computational biology and biochemistry ; v. 1

Language:

English

Subjects (All):

Genetics--Data processing.

Genetics.

Neural networks (Computer science).

Bioinformatics.

Genomes.

Physical Description:

1 online resource (218 p.)

Edition:

1st ed.

Place of Publication:

New York : Elsevier Science B.V., c2000.

Language Note:

English

Summary:

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.

Contents:

Machine generated contents note: 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 5.3 Unsupervised Learning 5.4 Software for Training Neural Networks, 5.5 References PART III Genome Infornatics Applications CHAPTER 6 Design Issues - Feature Presentation 6.1 Overview of Design Issues 6.2 Amino Acid Residues 6.3 Amino Acid Physicochemical and Structural Features 6.4 Protein Context Features and Domains 6.5 Protein Evolutionary Features 6.6 Feature Representation 6.7 References CHAPTER 7 Design Issues - Data Encoding 7.1 Direct Input Sequence Encoding 7.2 Indirect Input Sequence Encoding 7.3 Construction of Input Layer 7.4 Input Trimming 7.5 Output Encoding 7.6 References CHAPTER 8 Design Issues - Neural Networks 8.1 Network Architecture. 8.2 Network Learning Algorithm 8.3 Network Parameters 8.4 Training and Test Data 8.4.1 Network Generalization 8.4.2 Data Quality and Quantity 8.4.3 Benchmarking Data Set 8.5 Evaluation Mechanism 8.6 References CHAPTER 9 Applications - Nucleic Acid Sequence Analysis 9.1 Introduction 9.2 Coding Region Recognition and Gene Identification. 9.3 Recognition of Transcriptional and Translational Signals 9.4 Sequence Feature Analysis and Classification 9.5 References CHAPTER 10 Applications - Protein Structure Prediction 10.1 Introduction 10.2 Protein Secondary Structure Prediction 10.3 Protein Tertiary Structure Prediction Protein Distance Constraints 10.4 Protein Folding Class Prediction 10.5 References CHAPTER 11 Applications - Protein Sequence Analysis 11.1 Introduction 11.2 Signal Peptide Prediction 11.3 Other Motif Region and Site Prediction 11.4 Protein Family Classification 11.5 References Part IV Open Problems and Future Directions CHAPTER 12, Integration of Statistical Methods into Neural Network Applications 12.1 Problems in Model Development 12.1.1 Input Variable Selection 12.1.2 Number of Hidden Layers and Units 12.1.3 Comparison of Architectures 12.1.4 Need for Benchmark Data 12.2 Training Issues, 12.3 Interpretation of Results 12.4 Further Sources of Information 12.5 References CHAPTER 13 Future of Genome Informatics Applications 13.1 Rule and Feature Extraction from Neural Networks 13.2.1 Rule Extraction from Pruned Networks 13.2.2 Feature Extraction by Measuring Importance of Inputs 13.2.3 Feature Extraction Based on Variable Selection 13.2.4 Network Understanding Based on Output Interpretation, 13.2 Neural Network Design Using Prior Knowledge 13.3 Conclusions 13.4 References Glossary Author Index Subject Index.

Notes:

Description based upon print version of record.

Includes bibliographical references and indexes.

ISBN:

1-281-07041-6

9786611070410

0-08-053737-5

OCLC:

476114505