Table Of ContentTomaszG.Smolinski,MariofannaG.Milanova,andAboul-EllaHassanien(Eds.)
ComputationalIntelligenceinBiomedicineandBioinformatics
StudiesinComputationalIntelligence,Volume151
Editor-in-Chief
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SoftwareEngineering,ArtificialIntelligence,Networkingand
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DesignandAnalysisofLearningClassifierSystems,2008 ISBN978-3-540-70774-5
ISBN978-3-540-79865-1
Vol.151.TomaszG.Smolinski,MariofannaG.Milanova
Vol.140.NadiaMagnenat-Thalmann,LakhmiC.Jain andAboul-EllaHassanien(Eds.)
andN.Ichalkaranje(Eds.) ComputationalIntelligenceinBiomedicineandBioinformatics,
NewAdvancesinVirtualHumans,2008 2008
ISBN978-3-540-79867-5 ISBN978-3-540-70776-9
Tomasz G.Smolinski
Mariofanna G.Milanova
Aboul-Ella Hassanien
(Eds.)
Computational Intelligence in
Biomedicine and Bioinformatics
Current Trends and Applications
123
Dr.TomaszG.Smolinski ProfessorAboul-EllaHassanien
DepartmentofBiology DepartmentofQuantitativeMethodsand
EmoryUniversity InformationSystems
1510CliftonRd.NE CollegeofBusinessandAdministration
Atlanta,Georgia30322 KuwaitUniversity
USA P.O.Box5486
Email:[email protected] Safat,13055
Kuwait
Email:[email protected]
and
ProfessorMariofannaG.Milanova DepartmentofInformationTechnology
DepartmentofComputerScience FacultyofComputersandInformation
UniversityofArkansasatLittleRock CairoUniversity
2801S.UniversityAve. 5AhamedZewalStreet
LittleRock,Arkansas72204 Orman,Giza
USA Egypt
Email:[email protected] Email:[email protected]
ISBN978-3-540-70776-9 e-ISBN978-3-540-70778-3
DOI10.1007/978-3-540-70778-3
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To
Jon and Tara
Tomasz G. Smolinski
To
my family
Mariofanna G. Milanova
To
my family
Aboul-Ella Hassanien
Preface
For the past decade or so, Computational Intelligence (CI) has been an ex-
tremely “hot” topic amongst researchers working in the fields of biomedicine
and bioinformatics. There are many successful applications of CI in such areas
ascomputationalgenomics,predictionofgeneexpression,proteinstructure,and
protein-protein interactions, modeling of evolution, or neuronal systems model-
ing and analysis. However, there still are many problems in biomedicine and
bioinformatics that are in desperate need of advanced and efficient computa-
tional methodologies to deal with tremendous amounts of data so prevalent in
thosekindsofresearchpursuits.Unfortunately,scientistsinboththesefieldsare
very often unaware of the abundance of computational techniques that could
be put to use to help them analyze and understand the data underlying their
research inquiries. On the other hand, computational intelligence practitioners
are often unfamiliar with the particular problems that their algorithms could
be successfully applied for. The separation between the two worlds is partially
causedby the use ofdifferent languages inthese two spheresofscience,but also
by a relatively small number of publications devoted solely to the purpose of
facilitating the exchange of new computational algorithms and methodologies
on one hand, and the needs of the realms of biomedicine and bioinformatics on
the other.
Inordertohelpfillthegapbetweenthescientistsonbothsidesofthisspectrum,
wehavesolicitedcontributionsfromresearchersactivelyapplyingcomputational
intelligencetechniquestoimportantproblemsinbiomedicineandbioinformatics.
The purpose of this book is to provide an overview of powerful state-of-the-art
methodologiesthatarecurrentlyutilizedforbiomedicine-and/orbioinformatics-
orientedapplications,sothatresearchersworkinginthosefieldscouldlearnofnew
methodstohelpthemtackletheirproblems.Ontheotherhand,wealsohopethat
the CI community willfindthis book usefulby discoveringa new andintriguing
areaofapplications.
We have divided the book into three major parts. Part I, Techniques and
Methodologies, contains a selection of contributions that provide a review of
several theories and methods that could be (or to some extent already are) of
VIII Preface
great benefit to practitioners in the fields of biomedicine and bioinformatics
dealingwithproblemsofdataexplorationandmining,search-spaceexploration,
optimization, etc.
In Chapter 1, Aboul ella Hassanien et al., present an overview of selected CI
techniques including Artificial Neural Networks (ANN), Particle Swarm Opti-
mization (PSO), Genetic Algorithms (GA), Fuzzy Sets (FS), and Rough Sets
(RS) and discuss their applications to several problems in bioinformatics and
computational biology, including gene selection, DNA fragment assembly, mul-
tiple sequence alignment, protein structure prediction, and human genetics.
Chapters 2, by Christopher M. Taylor and Arvin Agah, and 3 by Ray R.
Hashemi, Alexander A. Tyler, Azita A. Bahrami, deal with several methodolo-
gieswidelyusedforminingof,quitebroadlyspeaking,“bio-data.”Thediscussed
techniques andapproachesinclude roughand fuzzy sets,rule induction,and ge-
neticalgorithms.Theauthorsprovideintuitiveexamplestoexplainthepresented
methods andreview severalreal-life applications in the areasofanalysis ofgene
and protein expression, genome annotation, and mutations in cancer.
Concluding this part of the book is Chapter 4, by Nikola Kasabov et al., in
which the authors propose a novel ontology-based decision support framework
and a developmentplatform for the creationof globalknowledge representation
forlocalandpersonalizedmodelinganddecisionsupportinbiomedicalandbioin-
formatics applications. The authors discuss a case study on brain-gene-disease
ontology,wheretheyderivefromexistingdataandlocalprofilesofpatientsaset
of 12 genes related to the central nervous system cancer.Throughthis ontology
analysis, these genes are found to be related to different functions, areas, and
other diseases of the brain.
Part II of this book, Computational Intelligence in Biomedicine, contains a
collection of contributions on current state-of-the-artbiomedicalapplications of
CI.
Opening this section of the book is Chapter 5, by Samuel Neymotin et
al., which presents a time-domain algorithm to facilitate data-mining of large
electroencephalogram/electrocorticogramdatasets to identify the occurrence of
spike-waveorotheractivitypatterns.Theauthorssuccessfullyusedtheproposed
algorithmtoidentifyandclassifyactivityfrombothsimulatedandexperimental
seizures.
Chapter6,byFrank-Michael Schleif et al.,treatsonrecentextensionsofSelf-
Organizing Maps (SOM) as universal tools in the light of clinical proteomics.
The authors consider extensions of the standard SOMs and Learning Vector
Quantization (LVQs) for handling of more general metrics and propose a semi-
supervised approach and a fuzzy classification scheme based on prototypes for
classification of spectra.
Chapters 7, by Andrzej Obuchowicz et al., and 8, by Dongqing Chen et al.,
focus on state-of-the-art image processing techniques used in clinical applica-
tions. The first chapter presents an overview of CI methods applied to image
segmentation for cytopathology and the second introduces a novel anisotropic
Preface IX
3D surface evolution model for detecting protrusion shape based colonic polyps
on curved surfaces.
Chapter 9, by Feng Chu et al., describes a study on an application of a fuzzy
neural network (FNN) for cancer classification in microarray gene expression
data. The authors use three well-known microarray databases, i.e., the lym-
phoma data set, the small round blue cell tumor (SRBCT) data set, and the
ovarian cancer data set to test their approach. The results indicate that the
FNN classifier not only improves the accuracy of cancer classification problem,
but also helps find a better relationship between important genes and develop-
ment of cancers.
Finally,inChapter10,byBenjamin Haibe-Kains et al.,theauthorspresenta
retrospectiveclinicalstudywheretheadoptionofcomputationalintelligenceap-
proachesforperformingknowledgeextractionfromgeneexpressiondataenabled
an improved oncological clinical analysis.
PartIII,ComputationalIntelligenceinBioinformatics,openswithChapter11,
byVitoantonioBevilacquaetal.,whichpresentsanoverviewofartificialimmune
systems (AIS) in bioinformatics. The chapter describes how AIS have been suc-
cessfullyusedincomputationalbiologyproblemsandgivesreadersfurtherhints
aboutpossibleimplementationsinunexploredfields.
Chapters12,byHeitorSilv´erio Lopes,and13,byXiao-min Huetal.,treaton
applications of biology-inspired methods to the protein folding problem (PFP).
The first chapter provides a review of current developments in the area of ap-
plication of Evolutionary Algorithms (EA) to the PFP. The author discusses
several computational approaches for the PFP, from molecular dynamics and
approximationalgorithms to severalimplementations of EAs.The second chap-
ter presents an application of Ant Colony Optimization (ACO) to the flexible
PFP.The authorstest their proposedalgorithmonbenchmark two-dimensional
hydrophobic-polar(2D-HP)proteinsequencesandcompareitsperformancewith
some other well-known methods.
In Chapter 14, Kirt M. No¨el and Kay C. Wiese, advocate considering stem-
loops as sequence signals for finding ribosomal RNA genes. The authors devel-
oped an algorithm to identify stem-loops along a genomic sequence which are
similar to those found in rRNA secondary structures. The described results are
encouraging and demonstrate that stem-loops indeed have the potential to act
as sequence signals to discover rRNA genes.
Chapter15,byRadhakrishnan Nagarajan,dealswiththeanalysisofGenechip
oligonucleotide microarrays, in which gene expression estimation is given as a
complex combination of atomic entities on the array called probes. The study
investigatesqualitativesimilaritiesinthedistributionalsignaturesandlocalcor-
relation structures/patchiness between the perfect-match (PM) and mis-match
(MM) probe intensities. The results presented raise fundamental concerns in
interpreting Genechip oligonucleotide microarray data.
In Chapter 16, Vijayaraj Nagarajan and Mohamed O. Elasri describe and
discuss methods and tools used to predict structure and function of a puta-
tive protein sequence (Msa) with unknown function. The authors address the
X Preface
advantages and limitations of these approaches by using the Msa protein from
the human pathogen Staphylococcus aureus as a case study.
Finally, in Chapter 17, Deyu Zhou et al. discuss the importance for biomed-
ical researchers to be able to retrieve and mine specific knowledge from huge
quantity of published articles with high efficiency. The authors provide a road
map to the various information extraction methods in biomedical domain, such
as protein name recognition and discovery of protein-protein interactions, and
review current work and challenges in biomedical information extraction.
The editors are very grateful to the authors of the contributions included
in this volume and to the referees for their tremendous service by critically
reviewingthechapters.WewouldespeciallyliketothankProf.JanuszKacprzyk,
Editor-in-chiefoftheseries“StudiesinComputationalIntelligence,”Dr.Thomas
Ditzinger, Senior In-house Editor, and Ms. Heather King, Editorial Assistant
of Springer Verlag, Germany, for their help, editorial assistance, and excellent
cooperation. We sincerely hope that this book will prove useful to researchers
workingin biomedicine and bioinformatics as well as computationalintelligence
andthatitwillfacilitateaproductivedialogbetweenthecommunitiesandresult
in fruitful collaborations and scientific advancements on both sides.
January 2008 Tomasz G. Smolinski
Mariofanna G. Milanova
Aboul-Ella Hassanien
Contents
Part I: Techniques and Methodologies
1 Computational Intelligence in Solving Bioinformatics
Problems: Reviews, Perspectives, and Challenges
Aboul-Ella Hassanien, Mariofanna G. Milanova, Tomasz G. Smolinski,
Ajith Abraham.................................................... 3
2 Data Mining and Genetic Algorithms: Finding Hidden
Meaning in Biological and Biomedical Data
Christopher M. Taylor, Arvin Agah ................................. 49
3 The Use of Rough Sets as a Data Mining Tool for
Experimental Bio-data
Ray R. Hashemi, Alexander A. Tyler, Azita A. Bahrami ............... 69
4 Integrating Local and Personalised Modelling with Global
Ontology Knowledge Bases for Biomedical and Bioinformatics
Decision Support
Nikola Kasabov, Qun Song, Lubica Benuskova, Paulo Gottgtroy,
Vishal Jain, Anju Verma, Ilkka Havukkala, Elaine Rush, Russel Pears,
Alex Tjahjana, Yingjie Hu, Stephen MacDonell ....................... 93
Part II: Computational Intelligence in Biomedicine
5 Data-Mining of Time-Domain Features from Neural
Extracellular Field Data
Samuel Neymotin, Daniel J. Uhlrich, Karen A. Manning,
William W. Lytton................................................ 119
