Statistical and Machine Learning Approaches for Network Analysis PDF
Preview Statistical and Machine Learning Approaches for Network Analysis
STATISTICAL AND MACHINE LEARNING APPROACHES FOR NETWORK ANALYSIS STATISTICAL AND MACHINE LEARNING APPROACHES FOR NETWORK ANALYSIS Editedby MATTHIASDEHMER UMIT – The Health and Life Sciences University, Institute for Bioinformatics and TranslationalResearch,HallinTyrol,Austria SUBHASHC.BASAK NaturalResourcesResearchInstitute UniversityofMinnesota,Duluth Duluth,MN,USA Copyright©2012byJohnWiley&Sons,Inc.Allrightsreserved PublishedbyJohnWiley&Sons,Inc.,Hoboken,NewJersey PublishedsimultaneouslyinCanada Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedinanyformor byanymeans,electronic,mechanical,photocopying,recording,scanning,orotherwise,exceptas permittedunderSection107or108ofthe1976UnitedStatesCopyrightAct,withouteithertheprior writtenpermissionofthePublisher,orauthorizationthroughpaymentoftheappropriateper-copyfeeto theCopyrightClearanceCenter,Inc.,222RosewoodDrive,Danvers,MA01923,(978)750-8400,fax (978)750-4470,oronthewebatwww.copyright.com.RequeststothePublisherforpermissionshould beaddressedtothePermissionsDepartment,JohnWiley&Sons,Inc.,111RiverStreet,Hoboken,NJ 07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permission. LimitofLiability/DisclaimerofWarranty:Whilethepublisherandauthorhaveusedtheirbesteffortsin preparingthisbook,theymakenorepresentationsorwarrantieswithrespecttotheaccuracyor completenessofthecontentsofthisbookandspecificallydisclaimanyimpliedwarrantiesof merchantabilityorfitnessforaparticularpurpose.Nowarrantymaybecreatedorextendedbysales representativesorwrittensalesmaterials.Theadviceandstrategiescontainedhereinmaynotbesuitable foryoursituation.Youshouldconsultwithaprofessionalwhereappropriate.Neitherthepublishernor authorshallbeliableforanylossofprofitoranyothercommercialdamages,includingbutnotlimitedto special,incidental,consequential,orotherdamages. Forgeneralinformationonourotherproductsandservicesorfortechnicalsupport,pleasecontactour CustomerCareDepartmentwithintheUnitedStatesat(800)762-2974,outsidetheUnitedStatesat(317) 572-3993orfax(317)572-4002. 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LibraryofCongressCataloging-in-PublicationData: ISBN:978-0-470-19515-4 PrintedintheUnitedStatesofAmerica 10 9 8 7 6 5 4 3 2 1 ToChristina CONTENTS Preface ix Contributors xi 1 ASurveyofComputationalApproachestoReconstructand PartitionBiologicalNetworks 1 LipiAcharya,ThairJudeh,andDongxiaoZhu 2 IntroductiontoComplexNetworks:Measures, StatisticalProperties,andModels 45 KazuhiroTakemotoandChikooOosawa 3 ModelingforEvolvingBiologicalNetworks 77 KazuhiroTakemotoandChikooOosawa 4 ModularityConfigurationsinBiologicalNetworkswith EmbeddedDynamics 109 EnricoCapobianco,AntonellaTravaglione,andElisabettaMarras 5 InfluenceofStatisticalEstimatorsontheLarge-Scale CausalInferenceofRegulatoryNetworks 131 RicardodeMatosSimoesandFrankEmmert-Streib vii viii CONTENTS 6 WeightedSpectralDistribution:AMetricforStructural AnalysisofNetworks 153 DamienFay,HamedHaddadi,AndrewW.Moore,RichardMortier, AndrewG.Thomason,andSteveUhlig 7 TheStructureofanEvolvingRandomBipartiteGraph 191 ReinhardKutzelnigg 8 GraphKernels 217 MatthiasRupp 9 Network-BasedInformationSynergyAnalysisfor AlzheimerDisease 245 XueweiWang,HiroshaGeekiyanage,andChristinaChan 10 Density-BasedSetEnumerationinStructuredData 261 ElisabethGeorgiiandKojiTsuda 11 HyponymExtractionEmployingaWeightedGraphKernel 303 TimvorderBru¨ck Index 327 PREFACE Anemergingtrendinmanyscientificdisciplinesisastrongtendencytowardbeing transformedintosomeformofinformationscience.Oneimportantpathwayinthis transitionhasbeenviatheapplicationofnetworkanalysis.Thebasicmethodologyin thisareaistherepresentationofthestructureofanobjectofinvestigationbyagraph representingarelationalstructure.Itisbecauseofthisgeneralnaturethatgraphshave beenusedinmanydiversebranchesofscienceincludingbioinformatics,molecular andsystemsbiology,theoreticalphysics,computerscience,chemistry,engineering, drugdiscovery,andlinguistics,tonamejustafew.Animportantfeatureofthebook “StatisticalandMachineLearningApproachesforNetworkAnalysis”istocombine theoretical disciplines such as graph theory, machine learning, and statistical data analysis and, hence, to arrive at a new field to explore complex networks by using machinelearningtechniquesinaninterdisciplinarymanner. The age of network science has definitely arrived. Large-scale generation of genomic, proteomic, signaling, and metabolomic data is allowing the construction ofcomplexnetworksthatprovideanewframeworkforunderstandingthemolecular basisofphysiologicalandpathologicalstates.Networksandnetwork-basedmethods havebeenusedinbiologytocharacterizegenomicandgeneticmechanismsaswell asproteinsignaling.Diseasesarelookeduponasabnormalperturbationsofcritical cellularnetworks.Onset,progression,andinterventionincomplexdiseasessuchas canceranddiabetesareanalyzedtodayusingnetworktheory. Once the system is represented by a network, methods of network analysis can beappliedtoextractusefulinformationregardingimportantsystempropertiesandto investigateitsstructureandfunction.Variousstatisticalandmachinelearningmethods havebeendevelopedforthispurposeandhavealreadybeenappliedtonetworks.The purposeofthebookistodemonstratetheusefulness,feasibility,andtheimpactofthe ix x PREFACE methodsonthescientificfield.The11chaptersinthisbookwrittenbyinternationally reputedresearchersinthefieldofinterdisciplinarynetworktheorycoverawiderange oftopicsandanalysismethodstoexplorenetworksstatistically. Thetopicswearegoingtotackleinthisbookrangefromnetworkinferenceand clustering,graphkernelstobiologicalnetworkanalysisforcomplexdiseasesusing statistical techniques. The book is intended for researchers, graduate and advanced undergraduate students in the interdisciplinary fields such as biostatistics, bioinfor- matics, chemistry, mathematical chemistry, systems biology, and network physics. Eachchapteriscomprehensivelypresented,accessiblenotonlytoresearchersfrom thisfieldbutalsotoadvancedundergraduateorgraduatestudents. Many colleagues, whether consciously or unconsciously, have provided us with input, help, and support before and during the preparation of the present book. In particular,wewouldliketothankMariaandGheorgheDuca,FrankEmmert-Streib, BorisFurtula,IvanGutman,ArminGraber,MartinGrabner,D.D.Lozovanu,Alexei Levitchi, Alexander Mehler, Abbe Mowshowitz, Andrei Perjan, Ricardo de Matos Simoes,FredSobik,DongxiaoZhu,andapologizetoallwhohavenotbeennamed mistakenly.MatthiasDehmerthanksChristinaUhdeforgivingloveandinspiration. WealsothankFrankEmmert-Streibforfruitfuldiscussionsduringtheformationof thisbook. WewouldalsoliketothankoureditorSusanneSteitz-FillerfromWileywhohas been always available and helpful. Last but not the least, Matthias Dehmer thanks theAustrianScienceFunds(projectP22029-N13)andtheStandortagenturTirolfor supportingthiswork. Finally, we sincerely hope that this book will serve the scientific community of networksciencereasonablywellandinspirespeopletousemachinelearning-driven networkanalysistosolveinterdisciplinaryproblemssuccessfully. MatthiasDehmer SubhashC.Basak CONTRIBUTORS LipiAcharya, DepartmentofComputerScience,UniversityofNewOrleans,New Orleans,LA,USA Enrico Capobianco, Laboratory for Integrative Systems Medicine (LISM) IFC-CNR, Pisa (IT); Center for Computational Science, University of Miami, Miami,FL,USA Christina Chan, Departments of Chemical Engineering and Material Sciences, Genetics Program, Computer Science and Engineering, and Biochemistry and MolecularBiology,MichiganStateUniversity,EastLansing,MI,USA Ricardo de Matos Simoes, Computational Biology and Machine Learning Lab, CenterforCancerResearchandCellBiology,SchoolofMedicine,Dentistryand BiomedicalSciences,Queen’sUniversityBelfast,UK Frank Emmert-Streib, Computational Biology and Machine Learning Lab, CenterforCancerResearchandCellBiology,SchoolofMedicine,Dentistryand BiomedicalSciences,Queen’sUniversityBelfast,UK Damien Fay, Computer Laboratory, Systems Research Group, University of Cambridge,UK HiroshaGeekiyanage, GeneticsProgram,MichiganStateUniversity,EastLansing, MI,USA Elisabeth Georgii, Department of Information and Computer Science, Helsinki Institute for Information Technology, Aalto University School of Science and Technology,Aalto,Finland xi xii CONTRIBUTORS Hamed Haddadi, Computer Laboratory, Systems Research Group, University of Cambridge,UK Thair Judeh, Department of Computer Science, University of New Orleans, New Orleans,LA,USA ReinhardKutzelnigg, Math.Tec,Heumühlgasse,Wien,Vienna,Austria Elisabetta Marras, CRS4 Bioinformatics Laboratory, Polaris Science and TechnologyPark,Pula,Italy AndrewW.Moore, SchoolofComputerScience,CarnegieMellonUniversity,USA RichardMortier, HorizonInstitute,UniversityofNottingham,UK ChikooOosawa, DepartmentofBioscienceandBioinformatics,KyushuInstituteof Technology,Iizuka,Fukuoka820-8502,Japan Matthias Rupp, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany,and,InstituteofPureandAppliedMathematics,UniversityofCalifornia, LosAngeles,CA,USA;currentlyattheInstituteofPharmaceuticalSciences,ETH Zurich,Zurich,Switzerland. Kazuhiro Takemoto, Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan; PRESTO, Japan ScienceandTechnologyAgency,Kawaguchi,Saitama332-0012,Japan Andrew G. Thomason, Department of Pure Mathematics and Mathematical Statistics,UniversityofCambridge,UK Antonella Travaglione, CRS4 Bioinformatics Laboratory, Polaris Science and TechnologyPark,Pula,Italy Koji Tsuda, Computational Biology Research Center, National Institute of AdvancedIndustrialScienceandTechnologyAIST,Tokyo,Japan SteveUhlig, SchoolofElectronicEngineeringandComputerScience,QueenMary UniversityofLondon,UK TimvorderBru¨ck, DepartmentofComputerScience,TextTechnologyLab,Johann WolfgangGoetheUniversity,Frankfurt,Germany Xuewei Wang, Department of Chemical Engineering and Material Sciences, MichiganStateUniversity,EastLansing,MI,USA Dongxiao Zhu, Department of Computer Science, University of New Orleans; ResearchInstituteforChildren,Children’sHospital;TulaneCancerCenter,New Orleans,LA,USA
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