ARTIFICIAL NEURAL NETWORKS IN BIOLOGICAL AND ENVIRONMENTAL ANALYSIS A N A LY T I C A L C H E M I S T R Y S E R I E S 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 A N A LY T I C A L C H E M I S T R Y S E R I E S ARTIFICIAL NEURAL NETWORKS IN BIOLOGICAL AND ENVIRONMENTAL ANALYSIS Grady Hanrahan Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2011 by Taylor and Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S Government works Printed in the United States of America on acid-free paper 10 International Standard Book Number-13: 978-1-4398-1259-4 (Ebook-PDF) This book contains information obtained from authentic and highly regarded sources Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint Except as permitted under U.S Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers For permission to photocopy or use material electronically from this work, please access www.copyright com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400 CCC is a not-for-profit organization that provides licenses and registration for a variety of users For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com 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 Introduction 1.1 Artificial Intelligence: Competing Approaches or Hybrid Intelligent Systems? 1.2 Neural Networks: An Introduction and Brief History 1.2.1 The Biological Model 1.2.2 The Artificial Neuron Model 1.3 Neural Network Application Areas 11 1.4 Concluding Remarks 13 References 13 Chapter Network Architectures 17 2.1 2.2 Neural Network Connectivity and Layer Arrangement 17 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 Model Design and Selection Considerations 37 3.1 3.2 3.3 In Search of the Appropriate Model 37 Data Acquisition 38 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 3.5 Feature Selection 43 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 Intelligent Neural Network Systems and Evolutionary Learning 65 4.1 4.2 Hybrid Neural Systems 65 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 Applications in Biological and Biomedical Analysis 89 5.1 5.2 Introduction 89 Applications 89 ix Contents 5.2.1 5.2.2 Enzymatic Activity 94 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 Applications in Environmental Analysis 119 6.1 6.2 Introduction 119 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 183 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 Nortriptyline Olanzapine Omeprazole Ondansetron Oxybutynin Oxycodone Paclitaxel Pantoprazole Paroxetine Pentamidine Perhexiline Perphenazine Phenformin Phenylbutazone Phenytoin Pimozide Pindolol Pioglitazone Piroxicam Prednisolone Prednisone Progesterone Proguanil Promethazine Propafenone Propoxyphene Propranolol Quercetin 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 Yes Yes Yes Yes No No Yes Yes No No No No No No Yes No No Yes No No No Yes No No Yes No Yes No Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Drug Interaction Block (2008) Drug Interaction Manga (2005) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Manga (2005) (Continued) Appendix II 173 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 No Quetiapine Quinidine Quinine Rabeprazole Ramelteon Ranolzaine Remoxipride Repaglinide Retinoic acid Retinol Rifabutin Rifampicin Riluzole Risperidone Ritnavir Ropirinole Ropivacaine Rosiglitazone Salmeterol Saquinavir Sertindole Sertraline Sevoflurane Sibutramine Sildenafil Simvastatin Sirolimus Name 0 0 0 0 0 0 1 0 0 0 0 0 CYP1A2 0 0 0 0 0 0 0 0 0 0 0 0 CYP2C19 0 0 0 1 0 0 0 0 0 0 0 CYP2C8 0 0 0 0 0 0 0 0 0 0 0 0 CYP2C9 0 0 1 0 0 0 1 0 0 0 0 0 0 CYP2D6 0 0 0 0 0 0 0 0 0 0 0 0 0 CYP2E1 1 1 1 0 1 0 0 1 1 1 1 CYP3A4 Yes No No Yes Yes Yes No Yes No No No No No No Yes No No Yes No No No No No No Yes No No Multilabel Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Manga (2005) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Manga (2005) Manga (2005) Bonnabry (2001) Block (2008) Block (2008) Block (2008) Block (2008) Ref 174 Appendix II 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 Solifenacin Sorafenib Sparteine Sufentanil Sulfamethizole Sulfamethoxazole Sulfidimidine Sunitinib Suprofen Tacrine Tacrolimus Tadalafil Tamoxifen Tamsulosin Telithromycin Temazepam Teniposide Tenoxicam Terfenadine Testosterone Theophylline Thioridazine Timolol Tinidazole Tipranavir Tizanidine Tolbutamide Tolterodine 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 1 0 No No No No No No No No No No No No Yes Yes No No No No No No Yes Yes No No No No Yes Yes Block (2008) Block (2008) Manga (2005) Bonnabry (2001) Manga (2005) Block (2008) Manga (2005) Block (2008) Drug Interaction Block (2008) Block (2008) Block (2008) Block (2008) Bonnabry (2001) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) (Continued) Appendix II 175 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 No Torasemide Toremifene Tramadol Trazodone Triazolam Trimethoprim Trimetrexate Trimipramine Tropisetron Valdecoxib Valsartan Vardenafil Venlafaxine Verapamil Vinblastine Vincristine Vindesine Vinorelbine Voriconazole Warfarin-(R) Warfarin-(S) Zafirlukast Zaleplon Zidovudine Zileuton Ziprasidone Zolmitriptan Name 0 0 0 0 0 0 0 0 0 0 1 CYP1A2 0 0 0 0 0 0 0 0 0 1 0 0 0 CYP2C19 0 0 0 0 0 0 0 0 1 0 0 0 CYP2C8 0 0 0 1 0 0 0 0 0 0 CYP2C9 0 1 0 1 0 0 0 0 0 0 0 0 CYP2D6 0 0 0 0 0 0 0 0 0 0 0 0 0 CYP2E1 1 1 1 1 1 1 1 0 1 1 CYP3A4 No No Yes Yes No No No No Yes Yes No Yes No Yes No No No No Yes Yes No No No No Yes No No Multilabel Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Manga (2005) Block (2008) Bonnabry (2001) Bonnabry (2001) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Bonnabry (2001) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Block (2008) Manga (2005) Block (2008) Block (2008) Block (2008) Ref 176 Appendix II Zolpidem Zonisamide Zopiclone Zuclopenthixol 0 0 0 0 0 0 0 0 0 0 0 1 0 No No No No Source:â•… Michielan et al (2009) Journal of Chemical Information and Modeling 49: 12 With permission from the American Chemical Society 577 578 579 580 Block (2008) Block (2008) Block (2008) Drug Interaction Appendix II 177 Appendix III: A 143Member VOC Data Set and Corresponding Observed and Predicted Values of Air-toBlood Partition Coefficients Training Set 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Name KExp KANN KMLR KANN−KExp 1,1,1,2-Tetrachloroethane 1,1,1-Trichloroethane 1,1,2,2-Tetrachloroethane 1,1,2-Trichloroethane 1,2-Dichloroethane 1-Nitropropane 1-Butanol 1-Chloropropane 1-Pentanol 2,2,4-Trimethylpentane 2-Chloropropane 2-Methyl-1-propanol 2-Nitropropane 2-Propanol 4-Chlorobenzotrifluoride 4-Methyl-2-pentanone Propanone Bromochloromethane Bromodichloromethane Butyl acetate Butan-2-one Chlorodibromomethane Trichloromethane Cis-1,2,-dichloroethane Cyclohexane Decan Dichloromethane Diethyl ether Ethanol 1.55 0.63 2.13 1.67 1.39 2.31 3.08 0.59 2.83 0.23 0.32 2.92 2.23 3.02 1.43 1.96 2.36 1.21 1.49 1.94 2.24 1.88 1.15 1.16 0.17 1.47 1.12 1.11 3.27 1.45 0.69 2.21 1.58 1.32 2.41 3.07 0.52 3.03 0.24 0.31 2.98 2.08 2.86 1.22 2.03 2.55 1.23 1.45 1.71 2.21 1.76 1.13 1.21 0.19 1.64 0.98 1.11 3.33 0.97 0.93 0.97 1.04 1.05 2.97 2.67 0.89 2.66 0.92 0.92 2.71 2.97 2.81 2.42 1.76 1.35 0.47 0.83 2.34 1.58 1.00 1.31 1.07 0.37 1.68 0.23 1.96 2.97 −0.10 0.06 0.08 −0.09 −0.07 0.10 −0.01 −0.07 0.20 0.01 −0.01 0.06 −0.15 −0.16 −0.21 0.07 0.19 0.02 −0.04 −0.23 −0.03 −0.12 −0.02 0.05 0.02 0.17 −0.14 0.00 0.06 (Continued) 179 180 Training Set 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 Appendix III Name Ethyl acetate Ethylene oxide Hexane Isopentyl acetate Isopropyl acetate 2-Bromopropane Methanol Methyl tert-butyl ether Chloromethane Nonane o-Xylene Pentyl acetate Propyl acetate 1-Bromopropane p-Xylene Styrene Tert-amyl methyl ether Tetrachloroethene Toluene Trans-1,2-dichloroethene Tribromomethane Vinyl bromide Vinyl chloride 1,2,3-Trichloropropane 1,2,3-Trimethylbenzene 1,2-Dichlorobenzene 1,3,5-Trimethylbenzene 1,3-Dichlorobenzene 1-Chlorobutane 1-Chloropentane 1-Methoxy-2-propanol 2-Ethoxyethanol 2-Hexanone 2-Isopropoxyethanol 2-Methoxyethanol 3-Methylhexane 3-Pentanone Allylbenzene 1,1-Difluroethane Fluroxene Halopropane Cyclohexanone Dimethyl ether Divinyl ether Ethyl formate KExp KANN KMLR KANN−KExp 1.90 1.80 0.21 1.79 1.55 0.64 3.41 1.18 0.31 1.17 1.42 1.98 1.88 0.97 1.61 1.67 1.22 1.19 1.14 0.88 2.15 0.49 0.17 2.01 1.82 2.63 1.64 2.30 0.63 0.87 4.09 4.34 2.10 4.16 4.52 0.11 2.21 1.71 0.87 0.42 0.15 3.33 1.16 0.41 1.65 1.78 1.71 0.23 1.85 1.63 0.61 3.44 1.05 0.29 1.05 1.47 2.03 1.75 0.93 1.81 1.80 1.09 1.35 1.18 0.92 2.13 0.51 0.18 1.97 1.69 2.48 1.64 2.47 0.59 0.80 4.05 4.34 2.12 4.31 4.35 0.13 2.19 1.61 0.82 0.42 0.14 3.14 1.31 0.44 1.72 2.07 1.75 0.59 2.54 2.13 1.05 3.33 1.72 0.07 1.10 1.61 2.45 2.07 1.00 1.89 2.27 1.82 1.36 1.89 1.13 1.38 0.91 0.65 1.55 1.54 1.69 1.67 1.64 0.57 1.48 3.54 3.19 1.78 3.16 3.17 0.77 1.57 2.29 0.18 0.63 0.70 1.62 1.62 1.32 2.37 −0.12 −0.09 0.02 0.06 0.08 −0.03 0.03 −0.13 −0.02 −0.12 0.05 0.05 −0.13 −0.04 0.20 0.13 −0.13 0.16 0.04 0.04 −0.02 0.02 0.01 −0.04 −0.13 −0.15 0.00 0.17 −0.04 −0.07 −0.04 0.00 0.02 0.15 −0.17 0.02 −0.02 −0.10 −0.05 0.00 −0.01 0.15 0.15 0.03 0.07 181 Appendix III Training Set Name KExp KANN KMLR 1.25 0.83 3.37 2.23 0.32 1.30 0.89 3.19 2.43 0.34 1.92 1.00 2.14 1.67 0.65 0.05 0.06 −0.18 0.20 0.02 0.70 0.76 0.96 0.91 1.97 0.49 1.21 3.21 1.07 0.61 0.67 0.73 0.93 0.87 2.14 0.45 1.17 3.13 1.11 0.64 0.58 1.36 1.24 0.66 2.39 1.25 1.62 2.76 1.29 0.49 −0.03 −0.03 −0.03 −0.04 0.17 −0.04 −0.04 −0.08 0.04 0.03 0.57 0.76 0.52 1.72 3.22 0.72 0.24 1.87 0.20 1.06 0.71 0.82 0.39 0.73 0.52 0.71 0.55 1.59 3.13 0.69 0.26 1.86 0.22 1.07 0.75 0.78 0.42 0.71 0.86 0.31 0.25 2.02 2.50 0.43 0.39 0.70 0.73 1.17 0.89 1.34 0.75 1.45 −0.05 −0.05 0.03 −0.13 −0.09 −0.03 0.02 −0.01 0.02 0.01 0.04 −0.04 0.03 −0.02 104 105 106 107 Ethyl tert-pentyl ether Iodoethane Isophorone 3-Methylheptan-2-one 1,1-Dichloro-1fluoroethane 1,1-Dichloroethylene 1,2,4-Trifluorobenzene 1,2-Difluorobenzene 1,2-Dimethylcyclohexane 1,2-Epoxy-3-butene 1,3,5-Trifluorobenzene 1-Decene 1-Hexanol 1-Octene 1,1,1-Trifluoro-2,2dichloroethane 2,3,4-Trimethylpentane 2-Methylnonane 2-Methyloctance Bromobenzene Cyanoethylene oxide Cycloheptane Cyclopentane Dibromomethane Difluoromethane Fluorobenzene Fluorochloromethane Furane Hexafluorobenzene 2,3,4,5,6Pentafluorotoluene Pentafluorobenzene 4-Methylstyrene Tert-butylbenzene Tert-butylcyclohexane 0.51 2.37 1.24 1.16 0.48 2.22 1.28 1.02 0.66 2.42 2.01 1.06 −0.03 −0.15 0.04 −0.14 108 109 110 111 112 1,2-Dichloropane 2-Pentanone Isobutyl acetate Octane 2-Buthoxyethanol Test Set 1.14 2.14 1.69 0.68 3.90 1.18 2.38 1.80 0.72 4.02 1.35 1.69 2.30 0.95 3.38 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 KANN−KExp 0.04 0.24 0.11 0.04 0.12 (Continued) 182 Training Set 113 Appendix III Name 114 115 116 117 118 119 120 121 122 123 124 125 1,2,4Trimethylcyclohexane Chlorobenzene Benzene Chloroethane 1,4-Difluorobenzene 3-Methyl-1-butanol Methyl acetate Trichloroethene Pentachloroethane 1,2,4-Trimethylbenzene 1-Bromo-2-chloroethane 2-Methyl-2-propanol 3-Methylstyrene 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 1-Nonene Tert-pentyl alcohol Isopropyl benzene 2-Methylcyclohexanone Allyl chloride 1,3-Dimethylbenzene Methoxyflurane 2,3-Dimethylbutane 1,2-Dibromoethane 2-Methylheptane 2-Heptanone Methylcyclohexane Ethylbenzene 1,1-Dichloroethane 1-Propanol Heptane Propyl benzene Ethyl tert-butyl ether KExp KANN KMLR KANN−KExp 0.87 0.81 1.27 −0.06 1.63 1.05 0.49 0.87 2.75 1.98 1.14 2.02 1.47 1.60 2.68 2.28 1.85 1.12 0.50 0.82 2.53 2.06 1.21 2.10 1.71 1.44 2.48 2.12 1.77 1.15 0.69 0.81 2.15 2.45 1.06 2.40 1.66 1.34 2.75 2.49 0.22 0.07 0.01 −0.05 −0.22 0.08 0.07 0.08 0.24 −0.16 −0.20 −0.16 1.30 2.44 1.42 2.73 1.12 1.43 1.38 0.72 2.25 0.43 2.07 0.65 1.30 0.84 2.99 0.53 1.54 1.18 1.48 2.61 1.96 1.72 1.28 1.67 1.46 0.61 1.70 0.60 1.98 0.59 1.24 1.00 2.62 0.82 1.99 1.07 0.12 −0.15 −0.15 −0.14 −0.12 −0.16 0.10 −0.06 0.17 −0.06 −0.26 −0.05 −0.17 −0.04 −0.07 0.03 −0.13 0.11 Validation Set 1.18 2.59 1.57 2.87 1.24 1.59 1.28 0.78 2.08 0.49 2.33 0.70 1.47 0.88 3.06 0.50 1.67 1.07 Source: Konoz and Golmohammadi (2008) Analytical Chimica Acta 619: 157–164 With permission from Elsevier Nucleus Dendrites Axon Nucleus Dendrites Axon Soma Synaptic knob Synaptic knob Soma Figure 1.2â•… Biological neurons organized in a connected network, both receiving and sending impulses Four main regions comprise a neuron’s structure: the soma (cell body), dendrites, axons, and synaptic knobs (From Hanrahan, G 2010 Analytical Chemistry, 82: 4307–4313 With permission from the American Chemical Society.) K10638_CI.indd 12/6/10 7:37:58 PM x1 xn Figure 2.9â•… A Kohonen’s self-organizing map (SOM) displaying a feedforward structure with a single computational layer arranged in rows and columns For the input layer, each node is a vector representing N terms Each output node is a vector of N weights Upon visual inspection, colors are clustered into well-defined regions, with regions of similar properties typically found adjoining each other (From Hanrahan, G 2010 Analytical Chemistry, 82: 4307–4313 With permission from the American Chemical Society.) Parent Parent 0100101 101011001 1100101 001001010 Offspring Offspring 0100101 001001010 1100101 101011001 Figure 4.3â•… Illustration of the single-point crossover process As depicted, the two parent chromosomes are cut once at corresponding points and the sections after the cuts swapped with a crossover point selected randomly along the length of the mated strings Two offspring are then produced K10638_CI.indd 12/6/10 7:38:00 PM MORT PCB28 PCB118 PCB138 PCB153 a_HCH b–HCH g_HCH hepta_Cl aldrine hepta_Cl_B pp_DDE op_DDD dieldrine endrine pp_DDD op_DDT pp_DDT HCB BkF FR IGN As Hg Pb V Fe Mn Al Li Figure 6.4â•… Representative subset of SOMs for 30 of the 44 parameters tested in chronic toxicity mode Make special note of the values from the mortality SOM, where a limited number of sites revealed high mortality (From Tsakovski et al 2009 Analytica Chimica Acta 631: 142–152 With permission from Elsevier.) MORT HITS 85 Group 1 Group 1 3 3 46 1 Group 2 1 Group d 7.07 1 Figure 6.5â•… The vicinities of four groups of sites that revealed high levels of mortality (From Tsakovski et al 2009 Analytica Chimica Acta 631: 142–152 With permission from Elsevier.) K10638_CI.indd 12/6/10 7:38:07 PM EC50 PCB28 PCB118 PCB138 PCB153 a_HCH b–HCH g_HCH hepta_Cl aldrine hepta_Cl_B pp_DDE op_DDD dieldrine endrine pp_DDD op_DDT pp_DDT HCB BkF FR IGN As Hg Pb V Fe Mn Al Li Figure 6.6â•… Representative subset of SOMs for 30 of the 44 parameters tested in acute toxicity mode The major indicator observed was EC50, whose values were indicative of the acute toxicity of the collected samples (From Tsakovski et al 2009 Analytica Chimica Acta 631: 142–152 With permission from Elsevier.) EC50 HITS 84.1 Group 2 2 1 d 2 31.1 57.6 Group Group 2 1 1 1 Figure 6.7â•… Highlight of three distinct groups of similar acute toxicity and object distribution (From Tsakovski et al 2009 Analytica Chimica Acta 631: 142–152 With permission from Elsevier.) K10638_CI.indd 12/6/10 7:38:12 PM ... Artificial Neural Networks in Biological and Environmental Analysis, Grady Hanrahan A N A LY T I C A L C H E M I S T R Y S E R I E S ARTIFICIAL NEURAL NETWORKS IN BIOLOGICAL AND ENVIRONMENTAL ANALYSIS. .. continued debate In writing Artificial Neural Networks in Biological and Environmental Analysis, my aim was to provide in- depth and timely perspectives on the fundamental, technological, and. .. continued interest in the use of neural network tools in scientific inquiry In the opening chapter, an introduction and brief history of computational neural network models in relation to brain