Table Of ContentStudies in Computational Intelligence 450
Editor-in-Chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
01-447Warsaw
Poland
E-mail:[email protected]
Forfurthervolumes:
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Tuan D. Pham and Lakhmi C. Jain (Eds.)
Knowledge-Based Systems
in Biomedicine
and Computational
Life Science
ABC
Editors
Prof.TuanD.Pham Dr.LakhmiC.Jain
AizuResearchClusterforMedical AdjunctProfessor
EngineeringandInformatics UniversityofCanberra
CenterforAdvancedInformation ACT2601
ScienceandTechnology Australia
TheUniversityofAizu
And
Aizuwakamatsu,Fukushima965-8580
Japan UniversityofSouthAustralia
Adelaide
SouthAustraliaSA5095
Australia
ISSN1860-949X e-ISSN1860-9503
ISBN978-3-642-33014-8 e-ISBN978-3-642-33015-5
DOI10.1007/978-3-642-33015-5
SpringerHeidelbergNewYorkDordrechtLondon
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(cid:2)c Springer-VerlagBerlinHeidelberg2013
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Preface
Expert systems, decision support systems, and knowledge-based systems have been
developedforclinicalapplicationsoverthedecades[1].Inparticular,theframeworkof
aknowledge-basedsystemhasbeenexploredtoembedasetofsystemsthatforreason-
ingondetailedknowledgeofa domain.Expertsystemsanddecisionsupportsystems
in medicine utilize techniquesofferedby Artificial Intelligence (AI) to provideinfer-
enceformakingdecisionsinaspecificclinicaldomainbasedonthepoolofstructured
expertknowledge.Knowledge-basedsystemsgobeyondtheprinciplesofAI toallow
flexibleincorporationofpriorknowledgeintothedecisionmakingframeworkofcom-
plexproblems.
Applications of knowledge-based systems to biomedical problems have recently
been reported as useful tools with both feasibility and advantage over conventional
methods[2].Knowledge-basedsystemsinbiomedicinedonotlimitthemselvestosym-
bolicinformationcomputingbutprovidestrategiesforembeddingspecificinformation
inavarietyofnumericalandanalyticalmethodologiescoveringimageanalysis,signal
processing, classification, and prediction. Advances in knowledge-basedlearning un-
deruncertaintywouldincreasetheimportantroleofknowledge-basedapproachwhich
equipscomputationallife-scienceresearcherswithabettertoolforgaininginsightinto
biomedicalcomplexsystemsinvolvinglarge-scaledatabases.
Given the increasingdemandforthe developmentof appropriateknowledge-based
systemsforcomputationallifesciences,reportsofsuchdevelopmentinsystemsbiology
andsystemsmedicinearestillrarelyfoundinliterature[3].Therapiddevelopmentof
biotechnologyhasresultedinavarietyofhigh-throughputandhigh-contentbiomedical
andbiologicaldataincludingbio-imaging,genomics,andproteomics;whichchallenge
conventionaldata analysis methods. One feasible solution is the utilization of expert
knowledgeandcastitintocorrespondingcomputationalmodels.Thenewbirthofthe
notionofpersonalizedmedicine,whichhasemergedasapopularconcept[4]–major
diseasessuchascancerandheartdiseasehaveageneticcomponent;thereforetheelu-
cidationofthehumangeneticcodeandensuingunderstandingofcellularprocessesat
themolecularlevelwillenablescientistsandphysicianstopredicttherelativeriskand
potentialtherapyforsuchconditionsonaperson-to-personbasis.Suchanambitionof
VI Preface
biomedicalbreakthroughwouldbeassistedbyappropriateknowledge-basedintelligent
systemsandbioinformatics[5].
This edited book consists of eight contributed chapters which reflect various de-
greesonthemostrecentdevelopmentsandapplicationsofknowledge-basedsystemsin
biomedicineandcomputationallifesciencecoveringtextmining,imageanalysis,and
signalprocessing.
Chapter 1 by Begum et al. introduces signal pre-processing and feature extraction
approachbasedonelectrocardiogramandfingertemperaturesensorsignalsforacase-
based reasoning for a personalized stress diagnosis system. This developmentcan be
usedasausefultoolformonitoringstress-relateddysfunctionsbothathomeandwork
withoutthesupervisionofclinicalstaff,andcanbeconsideredasanauxiliaryclinical
system.
Chapter 2 by Zhang et al. presents an image analysis system which utilizes the
knowledgeofeffectivefeaturesfordistinguishingbreastcancerfromcontrolsamples.
Theauthorsapplythecurvelettransform,graylevelco-occurrencematrixandlocalbi-
narypatterntocharacterizebreastcancerhistologicalimages;thenincorporaterandom
subspacebasedmultilayer-perceptronensembleforclassification.
Chapter3byYuetal.presentsanapproachforanalysisofneuronalcellimagesusing
the domain knowledge to establish a systematic study of the morphologyof cultured
brainneuronsinresponsetocellularchanges.Thecellimagesareeffectivelyextracted
byanovelsegmentation.Usingtheextractedresults,neuronskeletonsandtheiraxons
canbeautomaticallyanalyzedandquantified.
Chapter4discussesthemappingoffunctionalnetworkofproteinsequencesbyRun-
thala.Theauthorgivesanoverviewofvariouscomputationalalgorithmsforstructure
predictionofproteinincludingtheirstrengthandlimitation.Thechapteralsosuggests
possibledevelopmentsforbetterproteinmodelingmethodologies.
Chapter 5 by Lapish et al addresses an interesting issue on extracting high-quality
knowledge-basedinformationfromtextualdata for studyingthe relationshipbetween
thetwomentaldisorders:schizophreniaandalcoholism.Theauthorsutilizethedatabases
to identify common pathologies between these disease states to gain insight into the
comorbidity of the diseases, to which answer can be useful for treatment and drug
discovery.
Chapter 6 by Gavrishchaka and Senyukovapresents short time series of heart rate
variability to detect cardiac abnormalities.The detection appearsto be robustin han-
dlingcasesofcomplexcombinationofdifferentpathologies.Suchaclassificationtool
haspotentialforpersonalizedtreatmentandmonitoringofcardiacdisease.
Chapter 7 bySato et al. presentsan interestingresearchin holistic medicinebased
treatment of mental disorder with articular reference to depression and anxiety. The
studyiscarriedoutontheinteractionbetweenthetherapistandpatientbyfivesenses—
seeing, hearing, smelling, touching, and tasting. Measurements of the senses can be
recordedbyelectroencephalogramandanalyzedusingthetheoryofchaos.Preliminary
analysishasshownthatpatientswhoreceivedthefivesensestherapycanbeconsider-
ablyrelievedfromanxietyandmentaldisorder.
Preface VII
Chapter 8 by Ng et al. discusses a new approach for analysis of life-science data
basedonsubspaceclusteringwheretheinteractionbetweenfeaturesaremodeledusing
thetheoryoffuzzymeasuresandfuzzyintegrals.Themodelingoffeatureinteraction
is useful for multidimensional pattern recognition, where prior knowledge about the
degreesof importanceof individualfeaturesplaysan efficientrole in the formulation
offuzzyfeaturesandtheircombinations.Thischapterparticularlyexplorestheconcept
ofsignedfuzzymeasures.Interestingexperimentalresultsillustratethepotentialofthe
novelapproach.
Wewishtoexpressourgratitudetotheauthorsandreviewersfortheirexcellentcon-
tribution.ThankareduetotheSpringer-Verlagfortheirassistanceduringtheevolution
phaseofthemanuscript.
References
[1] E.H.Shortliffe,MedicalExpertSystems—KnowledgeToolsforPhysicians,WestJ
Med.,145(1986):830–839.
[2] C.A. Kulikowski, Knowledge-based Systems in Biomedicine: A 10-Year Retro-
spective,ProcAnnuSympComputApplMedCare,pp.423–424,1986.
[3] G.A.P.C. Burns, W.-C. Cheng, Tools for knowledge acquisition within the Neu-
roScholarsystemandtheirapplicationtoanatomicaltract-tracingdata,Journalof
BiomedicalDiscoveryandCollaboration,1(2006)10.DOI:10.1186/1747-5333-1-
10.
[4] G.S.GinsburgandJ.J.McCarthy,Personalizedmedicine:Revolutionizingdrugdis-
coveryandpatientcare,TrendsinBiotechnology,19(2001)491-496.
[5] G.Alterovitz,M.Ramoni,Editors,Knowledge-BasedBioinformatics:FromAnal-
ysistoInterpretation.JohnWileyandSons,WestSussex,UK,2010.
Contents
1 PhysiologicalSensorSignalsAnalysistoRepresentCases
inaCase-BasedDiagnosticSystem................................. 1
ShahinaBegum,MobyenUddinAhmed,PeterFunk
1 Introduction................................................ 1
2 ApplicationDomain:Stress................................... 3
2.1 PhysiologicalParameterstoMeasureStress ............. 4
3 MethodsandApproaches..................................... 7
3.1 Case-BasedReasoning ............................... 7
3.2 FuzzyLogic........................................ 10
3.3 FastFourierTransformation........................... 11
3.4 SensitivityandSpecificityAnalysis .................... 13
4 Knowledge-BasedStressDiagnosticSystem..................... 14
4.1 DataCollection ..................................... 14
4.2 FeatureExtraction................................... 15
4.3 FeatureExtractionfromFingerTemperatureSensor
Signals ............................................ 18
5 Evaluation ................................................. 19
6 Conclusions................................................ 22
References....................................................... 23
2 Breast Cancer HistologicalImage Classificationwith Multiple
FeaturesandRandomSubspaceClassifierEnsemble................. 27
YungangZhang,BailingZhang,WenjinLu
1 Introduction................................................ 27
2 ImageDataset .............................................. 28
3 FeatureExtraction........................................... 29
3.1 CurveletTransformforBreastCancerHistological
Image ............................................. 29
3.2 FeaturesExtractedfromGrayLevelCo-occurrence
Matrix............................................. 30
X Contents
3.3 CompletedLocalBinaryPatternsforTexture
Description......................................... 30
3.4 CombinedFeature................................... 32
4 RandomSubspaceEnsembleofNeuralNetworks ................ 32
4.1 RandomSubspaceEnsembleandMulti-layer
Perceptron ......................................... 33
4.2 TheoreticalAnalysisoftheEnsembleClassifier .......... 34
5 ExperimentsandResults ..................................... 36
5.1 EvaluationofIndividualClassifiers..................... 37
5.2 EvaluationofMLPRandomSubspaceEnsemble ......... 38
6 Conclusion................................................. 40
References....................................................... 41
3 ImageProcessingandReconstructionofCulturedNeuron
Skeletons........................................................ 43
DonggangYu,TuanD.Pham,JesseS.Jin,SuhuaiLuo,HongYan,
DenisI.Crane
1 Introduction................................................ 44
2 SegmentationofCulturedNeuronsUsingLogicalAnalysis
ofGreyandDistanceDifference............................... 44
2.1 NeuronalCellCultureandImageAcquisition............ 48
2.2 LogicalLevelTechnique ............................. 48
2.3 LogicalLevelTechniquewithDifferenceAnalysis
ofGreyRegion ..................................... 49
2.4 UseofFilteringWindowwithConstrained
Condition ......................................... 52
2.5 ExperimentResults.................................. 54
2.6 Discussion ......................................... 55
3 ReconstructionandExtractionofNeuronSkeletons............... 62
3.1 SmoothingofNeuronSkeletons ....................... 62
3.2 ReconstructionofNeuronSkeletons.................... 63
3.3 Analysisand CalculationofReconstructedNeuron
Skeletons .......................................... 68
4 ExperimentsandConclusion.................................. 72
References....................................................... 77
4 ProteinStructurePrediction:AreWeThereYet? .................... 79
AshishRunthala,ShibasishChowdhury
Abbreviations .................................................... 79
1 Introduction................................................ 80
2 CASP ..................................................... 81
3 ProteinModellingAlgorithms................................. 83
3.1 ab-initioApproach .................................. 83
3.2 ComparativeModelling .............................. 87
Contents XI
4 PredictingNewFolds........................................ 105
5 Applications ............................................... 107
6 FutureResearchDirections ................................... 108
7 Conclusion................................................. 110
References....................................................... 111
AppendixofComputationalMethods................................. 115
5 TextMiningforNeuroscience:ACo-morbidityCaseStudy ........... 117
ChristopherC.Lapish,NaveenTirupattur,SnehasisMukhopadhyay
1 Introduction................................................ 117
2 BackgroundLiterature ....................................... 119
3 Methodology............................................... 121
4 ResultsandDiscussion....................................... 123
4.1 Feasibility-ApplicationacrossThreeNeuroscience
Domains........................................... 123
4.2 AlcoholismandSchizophreniaCo-morbidity ............ 129
5 Conclusions................................................ 133
References....................................................... 134
6 RobustAlgorithmicDetectionofCardiacPathologiesfromShort
PeriodsofRRData............................................... 137
ValeriyV.Gavrishchaka,OlgaV.Senyukova
1 Introduction................................................ 137
2 ImportanceandChallengesofHRV-BasedCardiacDiagnostics
fromShortRRTimeSeries ................................... 139
3 GenericFrameworkfortheDiscoveryofRobustMulti-component
Indicators.................................................. 143
4 Meta-indicatorsforHRV-BasedDiagnostics..................... 145
5 Meta-classifiersforMultipleAbnormalityDetection.............. 147
6 DiagnosticsofComplexandRareEvents ....................... 149
7 Conclusions................................................ 152
References....................................................... 152
7 StudiesonFiveSensesTreatment .................................. 155
SadakaSato,TiejunMiao,MayumiOyama-Higa
1 Introduction................................................ 156
2 ExperimentMethod ......................................... 157
2.1 Experiment1 ....................................... 157
2.2 Experiment2 ....................................... 158
3 AnalysisMethod............................................ 159
3.1 ChaosAnalysisofTimeSeries ........................ 159
3.2 RecurrenceQuantitativeAnalysis ...................... 160
4 Results .................................................... 161
4.1 ResultsofChaosAnalysisofVoice..................... 161
4.2 ResultsofRQAAnalysis ............................. 162
4.3 ChaosandPowerSpectruminScalpEEG ............... 163
XII Contents
4.4 LyapunovExponentDistributionoverScalpEEG......... 166
4.5 ChangesofPlethysmograminRelationtoEEG .......... 168
5 CaseStudy................................................. 169
5.1 MethodofFiveSensesTherapy........................ 169
5.2 PatientsandStudyDesign ............................ 171
5.3 ResultofTherapy ................................... 172
6 DiscussionandConclusion ................................... 173
References....................................................... 174
8 FuzzyKnowledge-BasedSubspaceClusteringforLifeScienceData
Analysis......................................................... 177
TheamFooNg,TuanD.Pham,XiupingJia,DonaldFraser
1 Introduction................................................ 177
2 SoftSubspaceClustering..................................... 181
2.1 FWSCAlgorithms................................... 182
2.2 EWSCAlgorithms .................................. 183
3 Non-additiveMeasuresandChoquetIntegral .................... 185
3.1 FuzzyMeasures..................................... 185
3.2 SignedFuzzyMeasures .............................. 187
3.3 TheChoquetIntegral ................................ 187
4 SubspaceClustering-BasedChoquetIntegral .................... 188
4.1 FrameworkforFISC................................. 189
4.2 FrameworkforPFISC................................ 193
5 ExperimentalResults ........................................ 198
5.1 AnalysisofFeatureInteraction ........................ 202
5.2 PerformanceEvaluation .............................. 206
6 Conclusions................................................ 208
References....................................................... 209
AuthorIndex ........................................................ 215
