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Artificial Neural Networks.pdf
Artificial Neural Networks, Second Edition
Preface
Contents
Contributors
1 Introduction to the Analysis of the Intracellular Sorting Information in Protein Sequences: From Molecular Biology to Artificial Neural Networks
1 Introduction
1.1 How Do the Emergent Properties of Cells Arise from a Mix of Proteins, Lipids, Sugars, and Nucleic Acids? (See Note 1)
1.2 How Are Cargoes Selectively Loaded into the Right Vesicles and Delivered to the Right Compartments?
1.3 Can We Predict the Intracellular Destination of Transmembrane Proteins by Reading Their Amino Acid Sequences?
2 Method Foundations
2.1 Looking for Signals
2.2 Interpreting the Message
2.2.1 To Predict/Determine Signal-Adaptor Specificity: What Adaptor will Recognize a Given Signal?
2.2.2 To Predict/Determine Signal Relevance to Cargo Trafficking/Localization: What Is the Impact of Specific Adaptor-Signal Interactions for Cargo Trafficking/Localization?
2.3 Testing the Fate of Cargo
3 Protein Sequence Analysis Protocol
3.1 Finding the Amino Acid Sequence of the Cargo of Interest
3.2 Identification of the Cytosolic Region(s) Within the Cargo of Interest
3.3 Search for Motifs Fitting Sorting Signal Consensuses
3.4 Prioritize Isolated Motifs According to:
3.5 Predict Adaptor Recognition
3.6 Back-to-Context Analysis
4 Conclusion
4.1 Where to Go from Here?
4.2 Is Benchwork Still Needed?
5 Notes
References
2 Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N
1 Introduction
1.1 Relations Between Chemical Shifts and Protein Structure
1.2 Protein Backbone and Side-Chain Conformation from NMR Chemical Shifts
2 Materials
2.1 Software Requirements
2.2 Data Requirements
3 Methods
3.1 TALOS-N Prediction
3.2 Manual Inspection and Adjustment
3.3 Generation of Angular Restraints
4 Notes
References
3 Predicting Bacterial Community Assemblages Using an Artificial Neural Network Approach
1 Introduction
2 Materials
2.1 Collected Biological Observations
2.2 Required Computational Tools
3 Methods
3.1 Normalize Data from Biological Observations
3.2 Generate EIN from Normalized Data
3.3 Generate ANN from EIN
3.4 Implement MAP Model
3.5 Validate MAP model
4 Notes
References
4 A General ANN-Based Multitasking Model for the Discovery of Potent and Safer Antibacterial Agents
1 Introduction
2 General Procedure for the Setup of QSAR Models
2.1 Databases and Handling of the Retrieved Data
2.2 Molecular Descriptors and Their Applicability in QSAR Approaches
2.3 Modeling Techniques
3 An mtk-QSBER Model Combined with ANN for Virtual Screening of Antibacterial Agents
3.1 Curation of the ChEMBL Database
3.2 Moving Average Approach
3.3 Setup of the mtk-QSBER Model
3.4 Virtual Screening of Antibacterial Agents
4 Conclusions and Future Perspectives
References
5 Use of Artificial Neural Networks in the QSAR Prediction of Physicochemical Properties and Toxicities for REACH Legislation
1 Introduction
2 The Value of ANN-Derived QSARs
3 Do ANN-Derived QSARs Meet Validity Requirements for Regulatory Purposes?
4 Some Case Studies of ANN-Derived QSARs
4.1 Physicochemical Properties
4.1.1 Melting/Freezing Point
4.1.2 Boiling Point
4.1.3 Relative Density
4.1.4 Vapor Pressure
4.1.5 Surface Tension
4.1.6 Water Solubility
4.1.7 n-Octanol–Water Partition Coefficient
4.1.8 Flash Point
4.1.9 Flammability Limits
4.1.10 Explosive Properties
4.1.11 Self-Ignition Temperature (Autoignition Temperature)
4.1.12 Adsorption/Desorption
4.1.13 Dissociation Constant
4.1.14 Viscosity
4.1.15 Air–Water Partition Coefficient (Henry’s Law Constant)
4.2 Toxicological Properties
4.2.1 Skin Irritation or Corrosion
4.2.2 Eye Irritation
4.2.3 Skin Sensitization
4.2.4 Mutagenicity
4.2.5 Acute Toxicity (Mammalian)
4.2.6 Aquatic Toxicity
4.2.7 Degradation (Ready Biodegradability)
4.2.8 Acute Dermal Toxicity (Mammalian)
4.2.9 Short-Term Repeat-Dose Toxicity
4.2.10 Reproductive Toxicity
4.2.11 Toxicokinetics
4.2.12 Short-Term Fish Toxicity
4.2.13 Activated Sludge Respiration Inhibition
4.2.14 Abiotic Degradation (Hydrolysis)
4.2.15 Adsorption/Desorption
4.2.16 Sub-chronic Rodent Toxicity
4.2.17 Prenatal Developmental Toxicity
4.2.18 Two-Generation Reproductive Toxicity
4.2.19 Long-Term Aquatic Toxicity
4.2.20 Bioaccumulation in Aquatic Species
4.2.21 Effects on Terrestrial Organisms (Invertebrates, Microorganisms, Plants)
4.2.22 Carcinogenicity
4.2.23 Long-Term Effects on Terrestrial Organisms (Invertebrates, Microorganisms, Plants, Birds)
4.3 Software for Property and Toxicity Prediction
5 Conclusions
References
6 Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry
1 Introduction
2 Methods
2.1 Construction of Training and Testing Sets
2.2 ANN Structure
2.3 Training Results
2.4 Future Research
3 Notes
References
7 Prediction of Bioactive Peptides Using Artificial Neural Networks
1 Introduction
1.1 Peptides and Their Use in Medicinal Chemistry
1.2 Use of Prediction Models in the Search for New Drugs
2 Materials
3 Methods
3.1 Build Datasets
3.1.1 Build the Positive Dataset
3.1.2 Build the Negative Dataset
3.1.3 Merge Datasets and Add the Class Vector
3.1.4 Script Explanation
3.2 Select Descriptors
3.2.1 Descriptor Calculation
3.2.2 Script Explanation
3.3 Develop the Artificial Neural Network Model
3.3.1 Selection of the Training and Test Datasets
3.3.2 Training and Validation
3.3.3 Testing
3.4 Network Visualization
4 Notes
References
8 AutoWeka: Toward an Automated Data Mining Software for QSAR and QSPR Studies
1 Introduction
2 Materials
2.1 Data Source
2.2 Drawing and Refining Chemical Structures
2.3 Calculating Molecular Descriptors
2.4 Data Compilation
2.5 Multivariate Analysis
2.6 Plotting Graphs
3 Methods
3.1 Data Source
3.2 Drawing and Refining Chemical Structures
3.3 Calculating Molecular Descriptors
3.4 Data Compilation
3.5 Machine Learning Algorithms
3.5.1 Artificial Neural Network
3.5.2 Support Vector Machine
3.6 Multivariate Analysis
4 Notes
References
9 Ligand Biological Activity Predictions Using Fingerprint- Based Artificial Neural Networks (FANN-QSAR)
1 Introduction
2 Methods
2.1 Data Sets
2.2 Implementation of Fingerprint-Based Artificial Neural Network QSAR (FANN-QSAR)
2.3 Comparison of the FANN-QSAR Model with Other Methods
2.4 Cannabinoid Receptor Binding Activity Prediction for Known Cannabinoid Ligands
2.5 Cannabinoid Receptor Binding Activity Prediction on Newly Reported Cannabinoid Ligands
2.6 Virtual Screening of the NCI Compound Database for Lead Cannabinoid Ligands
3 Notes
References
10 GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction
1 Introduction
2 Types of Training and Prediction in GENN
2.1 Instance-Based Prediction
2.2 Window-Based Prediction
3 Database and Initialization File
4 Training and Prediction
4.1 Training a Model
4.2 Predictions from Trained Models
5 Special Options
5.1 Filter Network
5.2 Guided Learning
5.3 Global Inputs and Outputs
5.4 Degeneracy Training
5.5 Types of Output
6 Automated Learning
7 Examples
8 Summary
References
11 Modulation of Grasping Force in Prosthetic Hands Using Neural Network-Based Predictive Control
1 Introduction
2 Neural Network-Based Modeling
3 Optimization
4 Testing the Neural Network-Based NMPC on a Single Prosthetic Finger
5 Implementation on a Whole Hand Prototype
6 Notes
References
12 Application of Artificial Neural Networks in Computer-Aided Diagnosis
1 Introduction
2 Materials
2.1 ANN Architecture
2.2 Data Collection
2.3 Performance Evaluation
3 Methods
3.1 Medical Image Processing
3.2 Feature Extraction and Feature Selection
3.3 ANN Training
3.4 ROC Analysis and Observer Study
4 Notes
References
13 Developing a Multimodal Biometric Authentication System Using Soft Computing Methods
1 Introduction
2 Materials
2.1 Biometric Sensors
2.1.1 Voiceprint Sensors
2.1.2 Fingerprint Sensors
2.2 Sensor Data Acquisition Chain
2.2.1 Microphone Sensor Data Acquisition
2.2.2 Fingerprint Sensor Data Acquisition
2.3 Feature Extraction Algorithms
2.3.1 Voiceprint Hard Features
2.3.2 Voiceprint Soft Features
2.3.3 Fingerprint Hard Features
2.3.4 Fingerprint Soft Features
2.4 Hard Computing Pattern Matching
2.4.1 Voiceprint Hard Features
2.4.2 Fingerprint Hard Features
2.5 Soft Computing Pattern Matching
2.5.1 Artificial Neural Network (ANN)
2.6 Soft Computing Fusion and Decision
2.6.1 Fuzzy Logic Engine
2.7 System Modeling and Simulation Environments
2.7.1 ANN
2.7.2 FLE
2.8 Digital Signal Processor
3 Methods
3.1 Feature Extraction Layer
3.1.1 Voiceprint and Fingerprint Capture
3.1.2 Feature Extraction
3.2 Matching Layer
3.2.1 Enrollment Mode
3.2.2 Identifying Mode
3.2.3 Soft Biometric Training and Scoring
3.3 Decision Layer
3.4 Performance Evaluation
4 Notes
References
14 Using Neural Networks to Understand the Information That Guides Behavior: A Case Study in Visual Navigation
1 Introduction
2 Our Approach
3 Approach 1: ANN as a Classifier
4 Approach 2: Training an ANN Without Negative Examples
5 Approach 3: Learning and Discarding Views
6 Discussion and Future Directions
References
15 Jump Neural Network for Real-Time Prediction of Glucose Concentration
1 Introduction
2 The Diabetes Disease and Its Therapy
3 Glucose Prediction: A Brief State of the Art
4 A Jump Neural Network Methodology for Glucose Prediction
4.1 Inputs Selection and Preprocessing
4.2 Mathematical Representation of the Jump NN Model
4.3 Structure Optimization
4.4 NN Training Procedure
5 Database and Assessment Criteria
5.1 Database
5.2 Assessment Metrics
6 Results
7 Conclusions
References
16 Preparation of Ta-O-Based Tunnel Junctions to Obtain Artificial Synapses Based on Memristive Switching
1 Introduction
2 Materials
3 Methods
3.1 Substrate Preparation
3.2 Thin Film Deposition
3.3 Optical Lithography
3.4 Ion Beam Etching
3.5 Insulator
3.6 Contact Pads
4 Notes
References
17 Architecture and Biological Applications of Artificial Neural Networks: A Tuberculosis Perspective
1 Introduction
1.1 Human Brain
1.2 Architecture of Artificial Neural Networks
1.2.1 Feed-Forward Network
1.2.2 Feedback Network or Recurrent Network
1.3 Learning Algorithm of Neural Networks
1.3.1 Error-Correction Rule
1.4 Boltzmann Learning Rule
1.4.1 Hebbian Rule
1.4.2 Competitive Learning Rule
2 Biological Applications of ANNs
3 Methodology
4 Results
5 Discussion and Conclusions
References
18 Neural Networks and Fuzzy Clustering Methods for Assessing the Efficacy of Microarray Based Intrinsic Gene Signatures in Breast Cancer Classification and the Character and Relations of Identified Subtypes
1 Introduction
2 Materials
2.1 Dataset
3 Neural Networks and Fuzzy Clustering Methods
3.1 Self-Organizing Feature Map (SOM)
3.2 Emergent Self-Organizing Maps (ESOM)
3.3 Fuzzy Clustering by Local Approximation of MEmbership (FLAME)
3.4 Fuzzy C-Means (FCM)
4 Results and Discussion
4.1 Gene Clustering Based on the 71 and 93 Genes by SOM, ESOM, FCM, and FLAME
4.2 Classification of Subtypes: Clustering Patients Using SOM and FCM
4.2.1 SOM Clustering of Patients
4.2.2 FCM Clustering of Patients
5 Summary and Conclusions
References
19 QSAR/QSPR as an Application of Artificial Neural Networks
1 Introduction
2 Methodology
2.1 Software
3 QSAR/QSPR
3.1 QSAR-ANN Application
3.2 QSPR-ANN Application
4 Conclusions and Perspectives
References
Index
Methods in
Molecular Biology 1260
Hugh Cartwright Editor
Arti cial
Neural
Networks
Second Edition
METHODS IN MOLECULAR BIOLOGYSeries EditorJohn M. WalkerSchool of Life SciencesUniversity of HertfordshireHat fi eld, Hertfordshire, AL10 9AB, UK For further volumes: http://www.springer.com/series/7651
Artificial Neural NetworksSecond Edition Edited by Hugh CartwrightChemistry Department, Oxford University, Oxford, UK; Chemistry Department, University of Victoria, BC, Canada
Additional material to this book can be downloaded from http://extras.springer.com ISSN 1064-3745 ISSN 1940-6029 (electronic)ISBN 978-1-4939-2238-3 ISBN 978-1-4939-2239-0 (eBook) DOI 10.1007/978-1-4939-2239-0 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2014956521 © Springer Science+Business Media New York 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifi cally for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Humana Press is a brand of SpringerSpringer is part of Springer Science+Business Media (www.springer.com) Editor Hugh Cartwright Chemistry Department Oxford University Oxford , UK Chemistry Department University of Victoria BC , Canada
v Artifi cial Neural Networks (ANNs) are among the most fundamental techniques within the fi eld of Artifi cial Intelligence. Their operation loosely emulates the functioning of the human brain, but the value of an ANN extends well beyond its role as a biological model. An ANN can both memorize and reason: it provides a way in which a computer can learn from scratch about a previously unseen problem. Remarkably, the exact form of the prob-lem is rarely critical; it might be fi nancial (e.g., can we predict the direction of the stock market in the next few months?); it might be sociological (what factors make a face attrac-tive?); it could be medical (can we tell from an X-ray whether a bone is broken?); or, as in this volume, the problem might be purely scientifi c. This text brings together some productive and fascinating examples of how ANNs are applied in the biological sciences and related areas: from the analysis of intracellular sorting information to the prediction of the behavior of bacterial communities; from biometric authentication to studies of tuberculosis; from studies of gene signatures in breast cancer classifi cation to the use of mass spectrometry in metabolite identifi cation; from visual navi-gation to computer diagnosis of possible lesions; and more. The authors describe not only what they have done with ANNs but also how they have done it. Readers intrigued by the work described in this book will fi nd numerous practical details, which should encourage further use of these rapidly developing tools. Oxford, UK Hugh Cartwright Pref ace
vii Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction to the Analysis of the Intracellular Sorting Information in Protein Sequences: From Molecular Biology to Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 R. Claudio Aguilar 2 Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N. . . . . . . . . . . . . . . . . . . . . . . . . 17 Yang Shen and Ad Bax 3 Predicting Bacterial Community Assemblages Using an Artificial Neural Network Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Peter Larsen , Yang Dai , and Frank R. Collart 4 A General ANN-Based Multitasking Model for the Discovery of Potent and Safer Antibacterial Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 A. Speck-Planche and M. N. D. S. Cordeiro 5 Use of Artificial Neural Networks in the QSAR Prediction of Physicochemical Properties and Toxicities for REACH Legislation . . . . . . . 65 John C. Dearden and Philip H. Rowe 6 Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 J. H. Miller , B. T. Schrom , and L. J. Kangas 7 Prediction of Bioactive Peptides Using Artificial Neural Networks. . . . . . . . . . 101 David Andreu and Marc Torrent 8 AutoWeka: Toward an Automated Data Mining Software for QSAR and QSPR Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Chanin Nantasenamat , Apilak Worachartcheewan , Saksiri Jamsak , Likit Preeyanon , Watshara Shoombuatong , Saw Simeon , Prasit Mandi , Chartchalerm Isarankura-Na-Ayudhya , and Virapong Prachayasittikul 9 Ligand Biological Activity Predictions Using Fingerprint- Based Artificial Neural Networks (FANN-QSAR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Kyaw Z. Myint and Xiang-Qun Xie 10 GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction . . . . . . . . . . . . . . . . . . . . . . 165 Eshel Faraggi and Andrzej Kloczkowski 11 Modulation of Grasping Force in Prosthetic Hands Using Neural Network-Based Predictive Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Cristian F. Pasluosta and Alan W. L. Chiu 12 Application of Artificial Neural Networks in Computer- Aided Diagnosis . . . . . 195 Bei Liu Contents
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