Artificial Neural Networks

353 Pages·2015·9.59 MB·English

by  Hugh Cartwright (eds.)

Methods in
Molecular Biology 1260
Hugh Cartwright Editor
Artifi cial
Neural
Networks
Second Edition
M M B
ETHODS IN OLECULAR IOLOGY
Series Editor
John M. Walker
School of Life Sciences
University of Hertfordshire
Hat fi eld, Hertfordshire, AL10 9AB, UK
For further volumes:
http://www.springer.com/series/7651

Artificial Neural Networks
Second Edition
Edited by
Hugh Cartwright
Chemistry Department, Oxford University, Oxford, UK; Chemistry Department,
University of Victoria, BC, Canada
Editor
Hugh Cartwright
Chemistry 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 2 015
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 Springer
Springer is part of Springer Science+Business Media (www.springer.com)
Pref ace
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 H ugh Cartwright
v

Contents
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5
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. R owe
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. . . . . . . . . . 1 01
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-B ased 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-A ided Diagnosis . . . . . 195
Bei Liu
vii
viii Contents
13 Developing a Multimodal Biometric Authentication System
Using Soft Computing Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Mario Malcangi
14 Using Neural Networks to Understand the Information
That Guides Behavior: A Case Study in Visual Navigation. . . . . . . . . . . . . . . . 227
Andrew Philippides , Paul Graham , Bart Baddeley , and Philip Husbands
15 Jump Neural Network for Real-Time Prediction
of Glucose Concentration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
Chiara Zecchin , Andrea Facchinetti , Giovanni Sparacino ,
and Claudio Cobelli
16 Preparation of Ta-O-Based Tunnel Junctions to Obtain Artificial
Synapses Based on Memristive Switching. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Stefan Niehörster and Andy Thomas
17 Architecture and Biological Applications of Artificial Neural Networks:
A Tuberculosis Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 69
Jerry A. Darsey, William O. Griffin, Sravanthi Joginipelli,
and Venkata Kiran Melapu
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 85
S andhya Samarasinghe and Amphun Chaiboonchoe
19 QSAR/QSPR as an Application of Artificial Neural Networks. . . . . . . . . . . . . 3 19
Narelle Montañez-Godínez , Aracely C. Martínez-Olguín , Omar Deeb ,
Ramón Garduño-Juárez , and Guillermo Ramírez-Galicia
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
Contributors
R. CLAUDIO AGUILAR • Department of Biological Sciences, Purdue University ,
West Lafayette, IN , USA
DAVID ANDREU • Department of Experimental and Health Sciences, Universitat Pompeu
Fabra , Barcelona, Spain
BART BADDELEY • Centre for Computational Neuroscience and Robotics , University
of Sussex , Brighton , UK
AD BAX • Laboratory of Chemical Physics, National Institute of Diabetes and Digestive
and Kidney Diseases, National Institutes of Health , Bethesda, MD , USA
AMPHUN CHAIBOONCHOE • Integrated Systems Modelling Group , Lincoln University ,
Christchurch, New Zealand ; Division of Science and Math, and Center for Genomics
and Systems Biology, New York University Abu Dhabi , Abu Dhabi, UAE
ALAN W. L. CHIU • Biomedical Engineering, Rose-Hulman Institute of Technology ,
Terre Haute, IN , USA
CLAUDIO COBELLI • Department of Information Engineering , University of Padova ,
Padova , Italy
FRANK R. COLLART • Biosciences Division, Argonne National Laboratory , Lemont, IL , USA
M. N. D. S. CORDEIRO • Department of Chemistry and Biochemistry, Faculty of Sciences,
University of Porto , Porto , Portugal
YANG DAI • Department of Bioengineering, University of Illinois at Chicago , Chicago ,
IL , USA
JERRY A. DARSEY • Department of Chemistry , University of Arkansas at Little Rock ,
Little Rock, AR , USA
JOHN C. DEARDEN • School of Pharmacy & Biomolecular Sciences, Liverpool John Moores
University , Liverpool, UK
OMAR DEEB • Faculty of Pharmacy, Al-Quds University , Jerusalem, Palestine
ANDREA FACCHINETTI • Department of Information Engineering , University of Padova ,
Padova , Italy
ESHEL FARAGGI • Department of Biochemistry and Molecular Biology, Indiana University
School of Medicine , Indianapolis, IN , USA ; Battelle Center for Mathematical Medicine,
Nationwide Children’s Hospital , Columbus, OH , USA ; Physics Division, Research
and Information Systems , Carmel , IN , USA
RAMÓN GARDUÑO-JUÁREZ • Instituto de Ciencias Fisicas, Universidad Nacional Autonoma
de Mexico , Cuernavaca, Mexico
PAUL GRAHAM • Centre for Computational Neuroscience and Robotics , University of Sussex ,
Brighton , UK
WILLIAM O. GRIFFIN • Department of Applied Science, University of Arkansas at Little
Rock , Little Rock, AR , USA
PHILIP HUSBANDS • Centre for Computational Neuroscience and Robotics , University
of Sussex , Brighton , UK
CHARTCHALERM ISARANKURA-NA-AYUDHYA • Department of Clinical Microbiology
and Applied Technology, Faculty of Medical Technology , Mahidol University ,
Bangkok, Thailand
ix