Artificial Neural Networks in Biological and Environmental Analysis (Analytical Chemistry)

210 Pages·2011·2.49 MB·English

by  Grady Hanrahan

ARTIFICIAL NEURAL NETWORKS
IN BIOLOGICAL AND
ENVIRONMENTAL ANALYSIS
ANALYTICAL CHEMISTRY SERIES
Series Editor
Charles H. Lochmüller
Duke University
Quality and Reliability in Analytical Chemistry, George E. Baiulescu, Raluca-Ioana Stefan,
Hassan Y. Aboul-Enein
HPLC: Practical and Industrial Applications, Second Edition, Joel K. Swadesh
Ionic Liquids in Chemical Analysis, edited by Mihkel Koel
Environmental Chemometrics: Principles and Modern Applications, Grady Hanrahan
Quality Assurance and Quality Control in the Analytical Chemical Laboratory:
A Practical Approach, Piotr Konieczka and Jacek Namie´snik
Analytical Measurements in Aquatic Environments, edited by Jacek Namie´snik
and Piotr Szefer
Ion-Pair Chromatography and Related Techniques, Teresa Cecchi
Artificial Neural Networks in Biological and Environmental Analysis, Grady Hanrahan
ANALYTICAL CHEMISTRY SERIES
ARTIFICIAL NEURAL NETWORKS
IN BIOLOGICAL AND
ENVIRONMENTAL ANALYSIS
Grady Hanrahan
Boca Raton London New York
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To my dearest mother
In memory of Dr. Ira Goldberg

Contents
Foreword ...................................................................................................................xi
Preface....................................................................................................................xiii
Acknowledgments ....................................................................................................xv
The Author .............................................................................................................xvii
Guest Contributors ..................................................................................................xix
Glossary of Acronyms ............................................................................................xxi
Chapter 1 Introduction ..........................................................................................1
1.1 Artificial Intelligence: Competing Approaches or Hybrid
Intelligent Systems? ...................................................................1
1.2 Neural Networks: An Introduction and Brief History ...............3
1.2.1 The Biological Model ...................................................5
1.2.2 The Artificial Neuron Model .......................................6
1.3 Neural Network Application Areas .........................................11
1.4 Concluding Remarks ...............................................................13
References ..........................................................................................13
Chapter 2 Network Architectures .......................................................................17
2.1 Neural Network Connectivity and Layer Arrangement ..........17
2.2 Feedforward Neural Networks ................................................17
2.2.1 The Perceptron Revisited ...........................................17
2.2.2 Radial Basis Function Neural Networks ....................23
2.3 Recurrent Neural Networks .....................................................26
2.3.1 The Hopfield Network ................................................28
2.3.2 Kohonen’s Self-Organizing Map ................................30
2.4 Concluding Remarks ...............................................................33
References ..........................................................................................33
Chapter 3 Model Design and Selection Considerations ......................................37
3.1 In Search of the Appropriate Model ........................................37
3.2 Data Acquisition ......................................................................38
3.3 Data Preprocessing and Transformation Processes .................39
3.3.1 Handling Missing Values and Outliers ......................39
3.3.2 Linear Scaling ............................................................40
3.3.3 Autoscaling .................................................................41
3.3.4 Logarithmic Scaling ...................................................41
3.3.5 Principal Component Analysis ...................................41
3.3.6 Wavelet Transform Preprocessing ..............................42
vii
viii Contents
3.4 Feature Selection .....................................................................43
3.5 Data Subset Selection ..............................................................44
3.5.1 Data Partitioning ........................................................45
3.5.2 Dealing with Limited Data ........................................46
3.6 Neural Network Training ........................................................47
3.6.1 Learning Rules ...........................................................47
3.6.2 Supervised Learning ..................................................49
3.6.2.1 The Perceptron Learning Rule ...................50
3.6.2.2 Gradient Descent and Back-Propagation .....50
3.6.2.3 The Delta Learning Rule ............................51
3.6.2.4 Back-Propagation Learning Algorithm ......52
3.6.3 Unsupervised Learning and Self-Organization .........54
3.6.4 The Self Organizing Map ...........................................54
3.6.5 Bayesian Learning Considerations .............................55
3.7 Model Selection .......................................................................56
3.8 Model Validation and Sensitivity Analysis .............................58
3.9 Concluding Remarks ...............................................................59
References ..........................................................................................59
Chapter 4 Intelligent Neural Network Systems and Evolutionary Learning ......65
4.1 Hybrid Neural Systems ............................................................65
4.2 An Introduction to Genetic Algorithms ..................................65
4.2.1 Initiation and Encoding ..............................................67
4.2.1.1 Binary Encoding .........................................68
4.2.2 Fitness and Objective Function Evaluation ................69
4.2.3 Selection .....................................................................70
4.2.4 Crossover ....................................................................71
4.2.5 Mutation .....................................................................72
4.3 An Introduction to Fuzzy Concepts and Fuzzy
Inference Systems ....................................................................73
4.3.1 Fuzzy Sets ..................................................................73
4.3.2 Fuzzy Inference and Function Approximation ..........74
4.3.3 Fuzzy Indices and Evaluation of
Environmental Conditions .........................................77
4.4 The Neural-Fuzzy Approach ...................................................78
4.4.1 Genetic Algorithms in Designing Fuzzy
Rule-Based Systems ...................................................81
4.5 Hybrid Neural Network-Genetic Algorithm Approach ...........81
4.6 Concluding Remarks ...............................................................85
References ..........................................................................................86
Chapter 5 Applications in Biological and Biomedical Analysis .........................89
5.1 Introduction .............................................................................89
5.2 Applications .............................................................................89
Contents ix
5.2.1 Enzymatic Activity .....................................................94
5.2.2 Quantitative Structure–Activity Relationship
(QSAR) .......................................................................99
5.2.3 Psychological and Physical Treatment of
Maladies ...................................................................108
5.2.4 Prediction of Peptide Separation ..............................110
5.3 Concluding Remarks .............................................................112
References ........................................................................................115
Chapter 6 Applications in Environmental Analysis .........................................119
6.1 Introduction ...........................................................................119
6.2 Applications ...........................................................................120
6.2.1 Aquatic Modeling and Watershed Processes ...........120
6.2.2 Endocrine Disruptors ...............................................128
6.2.3 Ecotoxicity and Sediment Quality ...........................133
6.2.4 Modeling Pollution Emission Processes ..................136
6.2.5 Partition Coefficient Prediction ................................141
6.2.6 Neural Networks and the Evolution of
Environmental Change (A Contribution by
Kudłak et al.) ............................................................143
6.2.6.1 Studies in the Lithosphere ........................144
6.2.6.2 Studies in the Atmosphere ........................144
6.2.6.3 Studies in the Hydrosphere .......................145
6.2.6.4 Studies in the Biosphere ...........................146
6.2.6.5 Environmental Risk Assessment ..............146
6.3 Concluding Remarks .............................................................146
References ........................................................................................147
Appendix I: Review of Basic Matrix Notation and Operations .......................151
Appendix II: Cytochrome P450 (CYP450) Isoform Data Set Used in
Michielan et al. (2009) .........................................................................................155
Appendix III: A 143-Member VOC Data Set and Corresponding
Observed and Predicted Values of Air-to-Blood Partition Coefficients .........179
Index ......................................................................................................................183