10th International Symposium on Health Informatics and Bioinformatics

CONFERENCE BOOK

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organizedbyInformaticsInstitute

MiddleEastTechnicalUniversity(METU)

atMETUNorthernCyprusCampus

99738Kalkanli,Guzelyurt/NorthernCyprus

June28–30,2017

Motivation

The International SymposiumonHealth Informatics andBioinformatics, (HIBIT),nowinitstenthyear(HIBIT2017),aimstobringtogetheracademics,researchersandpractitionerswhoworkinthesepopularandfulfillingareasandtocreatethemuch-needed synergy among medical, biological and information technologysectors. HIBIT is one of the few conferences emphasizing such synergy. HIBITprovidesaforumfordiscussion,explorationanddevelopmentofboththeoreticaland practical aspects of health informatics and bioinformatics and a chance tofollowcurrentresearchinthisareabynetworkingwithotherbioinformaticians.

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HIBIT2017COMMITTEESHonoraryChairs

NeşeYalabık METUNazifeBaykal President,METUNorthernCyprusCampus

ConferenceCo-chairs

NurcanTunçbağ METUTuncaDoğan METU

OrganizationCommittee

AybarCanAcar METUÇağdaşSon METUNurcanTunçbağ METURengülAtalay METUTolgaCan METUTuncaDoğan METUYeşimAydınSon METU

ProgramCommittee

AbdullahKahraman UniversityofZurichAhmetSacan DrexelUniversityAhmetRaşitÖztürk UMassMedicalSchoolAlperKucukural UMassMedicalSchoolAnthonyGitter UniversityofWisconsin-MadisonArzucanOzgur BogaziciUniversityAttilaGursoy KoçUniversityAybarCanAcar METUBaharTaneri EasternMediterraneanUniversityBarışSüzek MuğlaSıtkıKoçmanUniversityBilgeKaracali İYTEBurakErman KoçUniversityBurcuBakir-Gungor AbdullahGulUniversityCerenSucularli HacettepeUniversityCagdasDevrimSon METUCesimErten AntalyaInternationalUniversity

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ElifErsonBensan METUEmreGüney UniversityofPompeuFabraErcümentÇiçek BilkentUniversityErdemKarabulut HacettepeUniversityErnestFraenkel MassachusettsInstituteofTechnologyGökhanKarakülah IBGHasanOğul BaşkentUniversityHilalKazan AntalyaInternationalUniversityJensAllmer İYTEMaria-JesusMartin EuropeanBioinformaticsInstitute(EMBL-EBI)MehmetÖzturk IBGMehmetSomel METUMesutMuyan METUNurcanTunçbağ METUOgünAdebali TheUniversityofNorthCarolinaatChapelHillOmerSinanSarac IstanbulTechnicalUniversityÖzgürGümüş EgeUniversityÖzgürŞahin BilkentUniversityÖzlemKeskin KoçUniversityÖzlenKonu BilkentUniversityÖznurTaştan BilkentUniversityRabieSaidi EuropeanBioinformaticsInstitute(EMBL-EBI)RalfHofestädt BielefeldUniversityRengülAtalay METUSukruTuzmen EasternMediterraneanUniversityTolgaCan METUTuğbaSüzek MuğlaSıtkıKoçmanUniversityTuncaDoğan METUUfukNalbantoğlu ErciyesUniversityUğurSezerman AcıbademUniversityVildaPurutcuoglu METUVolkanAtalay METUYeşimAydınSon METUZerrinIşık DokuzEylülUniversity

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HIBIT2017PROGRAM*

MainConference–Day1(June28,2017)

09:00–10:00

OpeningSession

§ WelcomebyAssoc.Prof.Dr.MohammadShikakhwa(AdvisortothePresident,METUNCC)

§ WelcomebyProf.Dr.NeseYalabik(METU,HIBIT2017HonoraryChair)

10:00–11:00

Keynote–Dr.ErnestFraenkel,MassachusettsInstituteofTechnology

Title:BeyondGenomics:OpportunitiesandChallengesforUsingtheOtherOmicsin

PrecisionMedicine

11:00–11:15 Coffeebreak&CancerGenomicsCloud(CGC)demobySevenBridgesGenomics

11:15–11:45Dr.EmreGuney,UniversityofPompeuFabra(InvitedTalk)

Title:Challengesandopportunitiesinsystemspharmacology

11:45–12:15Dr.ErcumentCicek,BilkentUniversity(Invited)

Title:GenomicDataSharingandPrivacyRisks

12:15–13:30 Lunch

13:30–14:00Dr.MehmetOzturk,InternationalBiomedicineandGenomeInstitute(Invited)

Title:CancerGenomeAnalysesandPersonalizedMedicine

14:00–14:30Dr.BedirhanUstun,KocUniversity(Invited)

Title:SemanticInteropretability:Howtoavoidane-TowerofBabel?

14:30–14:45 Coffeebreak&CancerGenomicsCloud(CGC)demobySevenBridgesGenomics

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14:45–15:30

SessionIStructuralBioinformatics(Amfi3)Chair:Dr.ÇağdaşSon

SessionIIHealthInformatics(Amfi2)Chair:Dr.YesimAydinSon

EzgiKaraca,JoãoRodrigues,Andrea

Graziadei,AlexandreBonvinandTeresa

Carlomagno.

Title:AnIntegrativeFrameworkfor

StructureDeterminationofMolecular

Machines

KorayAçıcı,ÇağatayBerkeErdaş,Tunç

Aşuroğlu,MünireKılınçToprak,Hamit

ErdemandHasanOğul.

Title:WearableSolutionsforParkinson’s

DiseaseMonitoring

SılaÖzdemir,AttilaGursoyandOzlem

Keskin.

Title:Analysisofsingleaminoacid

variationsinhotspotandhotregion

residuesofprotein-proteininteraction

interfaces

İlknurBuçanKırkbir,BurçinKurtand

KemalTurhan.

Title:AComputerAidedDiagnosisSystem

forHeartAttackUsingDecisionTree

SerenaMuratcıoğlu,HyunbumJang,Attila

Gursoy,OzlemKeskinandRuthNussinov.

Title:Interactionoffarnesylated,butlikely

depalmitoylated,RasisoformswithPDEδ

EmrahAkkoyun,AybarCanAcar,Byron

ZambranoandSeungikBaek.

Title:CardiovascularModellingfor

AbdominalAorticAneurysms

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15:30–16:15

SessionIIICancerSystemsBiology(Amfi3)Chair:Dr.MesutMuyan

SessionIVGenomics(Amfi2)Chair:Dr.OgünAdebali

OguzhanBegik,MerveOyken,TunaCinkilli

Alican,TolgaCanandAyseElifErsonBensan.

Title:Alternativepolyadenylationpatternsfor

cancerclassification

ArifYılmazandYeşimAydınSon.

Title:LosslessPruningofAHPSNP

PrioritizationTreeUsingRandom

ForestVariableImportances

TundeAderinwaleandHilalKazan.

Title:IntegratingMultipleDataTypesfor

CancerSubtypeDiscovery

FatihKaraoglanoglu,MarziehEslami

RasekhandCanAlkan.

Title:DiscoveringLargeGenomicInver-

sionsUsingLong-rangeInformation

MonaShojaei,EceAkhan,AybarCanAcarand

RengülÇetinAtalay.

Title:IdentificationofGeneMutations

InvolvedInDrugResistanceInLiverCancer

UsingRNA-SeqDataAnalysis

ElifBozlak,YetkinAlıcı,EvrimFer,

MelikeDönertaş,RasmusNielsen

andMehmetSomel.

Title:Neighbouringsequencecontext-

basedfixationbiasofmutationsin

chimpanzeegenome

16:15–16:30 CoffeeBreak&CancerGenomicsCloud(CGC)demobySevenBridgesGenomics

16:30–18:30 POSTERSESSION

19:00–20:30 WelcomeReception

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MainConference–Day2(June29,2017)

09:00–09:30 Tea/coffee,cookies

09:30–10:30Keynote–Dr.MariaMartin,EuropeanBioinformaticsInstitute(EMBL-EBI)

Title:Bigdatainfrastructureforadvancingbiomedicalresearch

10:30–10:45 Coffeebreak&CancerGenomicsCloud(CGC)demobySevenBridgesGenomics

10:45–11:15Dr.AybarCanAcar,METU(Invited)

Title:ProbabilisticProgramminginBioinformatics

11:15–11:45Dr.OgunAdebali,UniversityofNorthCarolinaatChapelHill(InvitedTalk)

Title:GenomicsofDNADamage,RepairandMutagenesis

11:45–12:15

SessionVMulti-omics(Amfi3)Chair:Dr.EmreGüney

SessionVIComputationalDrugDiscovery(Amfi2)Chair:Dr.TolgaCan

CesimErten,EvisHoxha,HilalKazanandEsra

Tepe.

Title:Identificationofdysregulatedpathways

acrossmultiplecancertypes

RemziÇelebi,ErkanYaşar,Özgür

GümüşandOğuzDikenelli.

Title:RDFGraphEmbeddingsfor

PredictionofDrug-DrugInteractions

GuvanchOvezmyradov.

Title:Integrativebioinformaticsanalysisof

multi-omicsdatafacilitatesfunctional

characterizationofthecandiatetumor

suppressorproteinCTCF

HandanMelikeDönertaşandJanet

Thornton.

Title:DrugRepurposingforAgeing:A

ConnectivityMapApproach

12:15–13:30 Lunch

13:30–14:00Dr.TugbaSuzek,MuglaSitkiKocmanUniversity(InvitedTalk)

Title:Bridgingcheminformaticsandbioinformaticswithpublictools

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14:00–14:30

SessionVIIProteinFunctionPrediction(Amfi3)Chair:Dr.BarisSuzek

SessionVIIIInteractomics&miRNA(Amfi2)Chair:Dr.AybarCanAcar

RabieSaidi,MaryamAbdollahyanand

MariaMartin.

Title:ASelf-trainingApproachforFunctional

AnnotationofUniProtKBProteins

ErdemTurkandBarisSuzek.

Title:LeveragingTaxonomicRanksfor

ImprovingMirrorTree-basedProtein

InteractionPrediction

AhmetSüreyyaRifaioğlu,MariaMartin,

RengulCetin-Atalay,VolkanAtalayand

TuncaDogan.

Title:InvestigationofMulti-taskDeepNeural

NetworksinAutomatedProteinFunction

Prediction

SedefErkunt,NecatiAltindis,

AhmetEfeKoseogluandCemalUn.

Title:UseofmiRNAinstrugglingof

Varroaparasite

14:30–14:45 Coffeebreak&CancerGenomicsCloud(CGC)demobySevenBridgesGenomics

14:45–17:00

SessionIX–DataSharing

(Amfi3)

Chair:Dr.RengulCetin-Atalay

SevenBridgesGenomics

Title:CancerGenomicsCloudandCaseStudies

Dr.RichardDixon,QIAGEN

Title:HumangenomesequenceanalysisandinterpretationwithQIAGENBioinformatics

Dr.ElifErsonBensan,METU

Title:Resolvingthecomplexityofthecancertranscriptomethrough3’UTRs

Dr.NurhanOzlu,KocUniversity

Title:ProteomicAnalysisofEpithelialMesenchymalTransition

Dr.HilalOzdag,AnkaraUniversity

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Title:SporadicColorectalCancerinTurkey:FromGWAStoValidation

Dr.OzgurSahin,BilkentUniversity

Title:SystemsbiologyapproachestoconstructRNAinteractionnetworksregulating

chemotherapyresistanceandmetastasisintriplenegativebreastcancer

Dr.MesutMuyan,METU

Title:Proteininteractionapproachestoassignafunctionforanestrogenresponsive

geneprotein:CXXC5

17:00–17:15 Closingremarks

ISCBRSGTurkeyStudentSymposium–Day3(June30,2017)

09:00–09:15 OpeningSession

09:15–10:00Keynote–Dr.UğurSezerman,AcıbademUniversity

Title:WANTED:Bioinformaticians

10:00–10:20 CoffeeBreak

10:20–11:20 StudentSession–ScienceSlam

11:20–11:50Dr.EmreGüney,UniversityofPompeuFabra(InvitedTalk)

Title:ShouldIstayorshouldIgoNOW?

11:50–12:10 Closingremarks

*Subjecttochanges

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KEYNOTES

Dr.ErnestFraenkel

MassachusettsInstituteofTechnology

http://fraenkel.mit.edu/

Prof. Fraenkel received his A.B. in Chemistry and Physics from HarvardCollegeandhisPh.D.inBiologyatthelaboratoryofProfessorCarlPabofromatMIT.Hecontinuedhispost-doctoralresearchasafellowatthelaboratoryof Professor Stephen Harrison at Harvard University. He was a Whitehead

FellowandaPfizerComputationalBiologyFellowattheWhiteheadInstitute.Prof.FraenkeljoinedMITasaResearchAffiliateattheMITComputerScienceandArtificialIntelligenceLaboratory.HebecameanAssistant Professor at the Department of Biological Engineering in 2006. The Fraenkel laboratory isdeveloping computational and experimental approaches to search for new therapeutic strategies fordiseases.Newexperimentalmethodsmakeitpossibletomeasurecellularchangesacrossthegenomeand proteome. These technologies include genome-wide measurements of transcription, of protein-DNA interactions (ChIP-Seq), of genetic interactions, and of protein modifications. Each data sourceprovidesaverynarrowviewofthecellularchanges.However,bycomputationallyintegratingthesedatathe group can reconstruct signaling pathways and identify previously unrecognized regulatorymechanismsthatcontributetotheetiologyofdiseaseandmayprovidenewapproachesfortreatment.

Dr.MariaMartin

EuropeanBioinformaticsInstitute(EMBL-EBI)

https://www.ebi.ac.uk/about/people/maria-jesus-martin

Maria’s team provides the bioinformatics infrastructure for the databasesandservicesoftheUniversalProteinResource(UniProt).Herteamcomprisessoftware engineers and bioinformaticians who are responsible for theUniProt,theGeneOntologyAnnotationandtheEnzymeportalsoftwareand

database development, and who study novel automatic methods for protein annotation andrepresentation. The team’s user experience analyst coordinates the user request gathering process,which informs the design and development of the web site. Maria’s team is responsible for themaintenance and development of tools for UniProt curation, and works in a fully complementaryfashionwithClaireO’Donovan’sUniProtContentteamtoprovideessentialresourcesforthebiologicalcommunity,asthedatabaseshavebecomeanintegralpartofthetoolsresearchersuseonadailybasisfortheirwork.

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KEYNOTETALKS

BeyondGenomics:OpportunitiesandChallengesforUsingtheOtherOmicsinPrecisionMedicine

ErnestFraenkelMassachusettsInstituteofTechnology

Rapidadvancesinhigh-throughputtechnologies,includingnext-generationsequencing,proteomics,andmetabolomics,areprovidingexceptionallydetaileddescriptionsofthemolecularchangesthatoccurindiseases.However,ithasbeendifficulttousethesedatatodiscovernewtherapeuticinsights.Despitetheir power, each of these methods still only captures a small fraction of the cellular response.Moreover,whendifferentassaysareappliedtothesameproblem,theyprovideapparentlyconflictinganswers.Iwillshowhowspecificnetworkmodelingapproachesrevealtheunderlyingconsistencyofthedata by identifying small, functionally coherent pathways linking the disparate observations. Thesepatient-specificnetworksmayprovidecriticalinsightsfortargetedtherapies.Currently,weareapplyingtheseapproachestoarangeofdiseases,includingbraintumorsandneurodegenerativedisorders.Iwilloutlinehowweareusingthesemethods inthe largeststudyeverstudyofALS,andwilldiscusssomeopenproblems.

Bigdatainfrastructureforadvancingbiomedicalresearch

MariaJesusMartinEuropeanMolecularBiologyLaboratory,EuropeanBioinformaticsInstitute(EMBL-EBI)

Over the last decade, life science research has become a data driven scientific field. Large-scalegenomics and proteomics research generate an enormous amount of information, which is mostlystored in knowledgebases, and accessed and analyzed using bioinformatics tools. Bioinformatics andsupporting databases hold significant interest in the scientific community because of its potential tomovescientific research forwardmorequicklyandat lessexpense than traditional laboratory testing.The European Bioinformatics Institute (EMBL-EBI) (http://www.ebi.ac.uk/) hosts many internationaldatabases and bioinformatics tools in major research areas, allowing data to be shared and freelyavailabletoall.Iwilldiscussthevitalroleofpublicdatabasesinlife-scienceresearchanddemonstratetheirusefromlabdatagenerationtosubmission,addedvalueanddissemination.Oneofworldleadingknowledgebases forproteinresearch isUniProt (http://www.uniprot.org/)whichprovideshighqualityand comprehensive protein information including annotation of variants with functional effects anddisease associations derived fromboth the scientific literature and large experimental data sets. Thisprovidesbiomedical researcherswithaplatformto investigateandvisualize functional informationofproteinsalongwithgenomicalterations thatcancontribute tomodifications in the translatedproteinandtheirsignificanceinadiseaseorsyndrome.Bioinformaticsintegrativeapproachesareessentialfortoday’shealthanddiseaseresearch-databasesareatthecoreprovidingdataandtoolsforbuildingnewplatformse.g.forclinicalanddrugdiscovery.

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INVITEDTALKS

Challengesandopportunitiesinsystemspharmacology

EmreGuneyUniversityofPompeuFabra

Thepastfewdecadeshavewitnessedaburstinthehigh-throughputmoleculardatasets,sheddinglightonthemechanismsunderlyinghumangeneticdiseases.Despitetherecenteffortsonsequencingandgenomewideassociationstudiescharacterizinggeneticvariantsassociatedwithpatientsubgroups,mostclinicaltrialsfailduetothelackofefficacyandsafety.Accordingly,repurposingexistingdrugsfornewuseshasattractedconsiderableattentionoverthepastyears.Toidentifypotentialcandidatesthatcouldberepositionedforanewindication,manystudiesmakeuseofchemical,target,andsideeffectsimilaritybetweendrugstotrainclassifiers.Despitepromisingprediction accuraciesof these supervised computationalmodels, their use inpractice,suchasforrarediseases, ishinderedbytheassumptionthattherearealreadyknownandsimilardrugs for a given condition of interest. In this talk, using publicly available data sets and data-drivenmodeling, I will challenge such assumptions and explain my recent efforts on interactome-basedcharacterizationofdrugeffect.

GenomicDataSharingandPrivacyRisks

ErcumentCicekBilkentUniversity

Genomic datasets are often associatedwith sensitive phenotypes and leak ofmembership information ismajorprivacyconcern.GenomicBeacons,whichwereproposedbyGlobalAllianceforGenomicsandHealthcoalition, aim to provide a secure and standardized interface for data sharing. Beacons allow only yes/noquestions on the presence of specific alleles in the dataset. However, recent studies have shown thatinformationleakispossiblebyrepeatedlyqueryingthebeaconforcertainSNPsandtheyalsoproposedsomecountermeasures.Inthistalk,Iwillpresentanovelre-identificationattackandshowthattheprivacyriskismore serious thanpreviously thought. In thismodel, even if the attackerdoesnothave access to the fullgenome of the victim (e.g., regions covering SNPswith lowminor allele frequencies are hidden), it is stillpossible to infer these alleles and infer beacon results with high confidence. We use the linkagedisequilibriumandahigh-orderMarkovchainbasedalgorithmfortheinference.Weshowthatinabeaconwith65 individuals fromtheCEUpopulation,wecan infermembershipof individualswith95%confidencewith only 2 queries, even though SNPs with MAFs less than 0.05 are hidden. We also show thatcountermeasures such as setting a query budget for the user would fail to protect the privacy of theparticipants.

CancerGenomeAnalysesandPersonalizedMedicine

MehmetÖzturkInternationalBiomedicineandGenomeInstitute

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SemanticInteropretability:Howtoavoidane-TowerofBabel?

BedirhanÜstünKocUniversity

Today many health systems utilize computers to capture and analyze health information digital format,however, exchange of information among different agents is very difficult because of use of differentterminologies,technicalformatsandlackofrelevantstandards:thissituationhasresultedinane-TowerofBabel.

Health Information emanates from different scientific activities using a large set of terms, definitions andotherformulationsrangingfromanatomytogenetics…;frommentalfunctionstosocialimpacts…andsoon.Therepresentationofthisbroadknowledgebaseforuseincomputerizedhealthinformationsystemsposeasetofchallengessuchas:thevastamountofconcepts;theirdifferentinterpretations;andvariationsinusebydifferentpeople includingprovidersandconsumers.Hence, the“standardization”ofhealth informationanditsterminologicalrepresentation.

Althoughstandardizationmaycreatea“commonlanguage”,itsemulationincomputersystemsrevealmanyshort-comings.“Meaningofmeaning”wasconceptualizedbyOgdenandRichardsin1923asthe“semantictriangle”dissectingtheknowledgerepresentationamongthethings,thoughtsandterms.Today,weneedtotransform the existing complex health knowledge from its analog format to digital format for use incomputerized health information systems. To achieve this aim, it is necessary to systematically create anexplicit and operational description of the concept to represent the knowledge. In computer science“ontologies”areusedforthispurposetorepresenteachconcept.Thetermontologyhereisuseddifferentthanitsuseinphilosophy,simplyasaknowledgerepresentationscience.

Oncethere isanagreementonthe“commonontology”thenthecomputersystemscantalktoeachotherusing this common framework and exchange information. Similarly, the same framework could be usedamongpeopleaswell toexchange information.This iscalled interoperabilityandhastwocomponents: (a)technical interoperability: one agent can send and receive messages from the other; (b) semanticinteroperability:themeaningofexchangedinformationiscommon;i.e.,understoodassamebetweentwoagents.

Nevertheless,conversionof“analog”healthrecordsinto“digital”willnotsufficetosolvetheproblem;andwilllikelyendupintheclassicalcomputeradage:“Garbage-in:Garbage-out”.Thereisaneedtomakeuseofthis information in an intelligent manner through computational processing. If we create appropriateaggregations, thenwecanprocessdata toanswerquestions like: (a)HowmanypatientsdohavediabetesmellitustypeIIinCyprus?(b)howmanyhavenormalHbA1cresults?(c)howmanyneedtreatmentneedfordiabetesmellitus?(d)HowdoesCyprusregionAcompareagainstregionB;orCyprusagainstrestofEuropeortheWorld?

HealthInformaticsofferagreatopportunitytomakeon-lineepidemiology:“e-pidemiology”oron-linehealthservicesresearch. Thisusagemustberegulatedbeyondandabovetechnicalusageregarding itsutility forpublichealth.Mostimportantlyappropriateuseofthisdatarespectingprivacyofpeopleisessential.Ontheotherhand, there isnext tononeregulatory framework tomandateanyorganizedactivity tomakeuseofthisdataforpublichealth.

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ProbabilisticProgramminginBioinformatics

AybarCanAcarMETU

GenomicsofDNADamage,RepairandMutagenesis

OgünAdebaliUniversityofNorthCarolinaatChapelHill

OurDNAiscontinuallydamagedbycellularandenvironmentalfactors.Someofthemostcommonmutagens,including UV light and platinum-based drugs, cause bulky adduct formation; these are almost exclusivelycorrected through nucleotide excision repair. Although the basic mechanisms of excision repair arereasonablywellunderstood,wepreviouslydidnotknowwhichgenomiclociarepreferentiallydamagedandrepaired.Usingthenoveltechnologies,Damage-seqandXR-seq,wewereabletodecipherthegenome-widekineticsand locationofdamage formationandexcisionrepair followingultraviolet irradiationandcisplatintreatments. These single-nucleotide resolution maps revealed cellular components affecting the DNAdamageformationandrepair.Thedamagesthatescapetherepairmachinerycanresult inmutagenesis. Inclinics, there are currently no accurate ways to determine the consequences of a novel mutation. Weproposeanewcomputationalperspectivetopredictingpathogenicityofsinglepointmutationsbybuildingthepreciseevolutionaryhistoriesof genes.Taken together,weapplygenomic technologies tounderstandhowDNAisdamagedandrepaired,aswellaswhetherunrepairedmutagenicdamagesleadtopathogenicityornot.

Bridgingcheminformaticsandbioinformaticswithpublictools

TuğbaSüzekMuglaSitkiKocmanUniversity

Typicaldrugdevelopmentpipelinetakesonaverage15yearswithextremelyhighmonetarycostswithverylow number of drugs successfully coming out of the process. Although not very well known bybioinformaticians, in-silico compound bioactivity mining tools offer a cost-effective alternative to leadcandidateidentification.Inthistalk,Iwillpresentanoverviewofonlinetoolsforin-silicodrugminingandanexampleonhowpubliccheminformatics resourcescanbeutilizedtobridgebetweencheminformaticsandbioinformaticsdataspace.

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INVITEDTALKS(DATASESSION)

CancerGenomicsCloudandCaseStudies

YağızCanŞişman

SevenBridgesGenomics

TheCancerGenomicsCloud (CGC),poweredbySevenBridges, isoneof threepilotsystems fundedbytheNationalCancerInstitutetoexploretheparadigmofcolocalizingmassivegenomicsdatasets,likeTheCancerGenomics Atlas (TCGA), alongside secure and scalable computational resources to analyze them. The CGCmakesmorethanapetabyteofmulti-dimensionaldataavailableimmediatelytoauthorizedresearchers.YoucanaddyourowndatatoanalyzealongsideTCGAusingpredefinedanalyticalworkflowsoryourowntools.Everyexecutionisfullyreproducible,andcollaboratingwithyourteamissimpleandsecure.

HumangenomesequenceanalysisandinterpretationwithQIAGENBioinformatics

RichardDixonQIAGEN

Resolvingthecomplexityofthecancertranscriptomethrough3’UTRs

ElifErsonBensanMETU

Advancements insequencingandtranscriptomeanalysismethodscontributedtoabetterunderstandingofthe complexity of cancer. These findings are paving the way toward the development of improveddiagnostics, prognostic predictions, and targeted treatment options. In an effort to have a morecomprehensiveunderstandingofcancer,wefocusonthe3’UTRs(untranslatedregions)ofgenesaswecametoappreciatethevalueofthenoncodingregionsofourgenomes,partlyduetomicroRNAs.The3’UTRshavelongbeenknowntohaveimportantrolesinmaintainingthestability,localization,andhalf-lifeofmRNAsbuta detailed mechanistic explanation as to how these properties are regulated or the consequences ofderegulation are just beginning to be appreciated. Our group is interested in the regulation andconsequencesof3’UTRlengthchangesinmRNAisoformsinbreastcancers.3’UTRlengthiscontrolledmainlyby the position of the polyadenylation (poly(A)) signal. Interestingly, majority of human genes harbormultiplepoly(A) signals on their 3’UTRs that canbedifferentially selectedon thebasis of thephysiologicstate of cells, resulting in alternative mRNA isoforms. Hence, deregulation of alternative polyadenylation(APA) has increasing interest in cancer research, because APA generates mRNA isoforms with potentiallydifferentprotein functions. Inthis talk,ourgroupsefforts to identifyandcharacterizenovelcancerrelatedgeneswill be discussed based on our combinatorial approach to reveal APA events. Overall, detection ofderegulated APA-generated isoforms in cancer may implicate some proto-oncogene activation cases of

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unknowncausesandmayhelpthediscoveryofnovelcases;thus,contributingtoabetterunderstandingofmolecularmechanismsofcancer.

ProteomicAnalysisofEpithelialMesenchymalTransition

NurhanÖzlüKocUniversity

Epithelial-Mesenchymal-Transition(EMT)playsanimportantroleduringcarcinogenesisandtumorformation.Itsmajorcontributiontothetumorformationisthroughprovidingepithelialcellswiththeabilityof invasionandmetastasis. By thisway, epithelial cells gain the capacity to disseminate from primary tumors to allowthemtogrowatadistantlocation.Cancerprogressionthroughtheprocessofmetastasishasbeenthefocusofextensiveresearchforyears.However,themolecularplayersoftheEMTprocessarenotfullyunderstood.Inthisregard,examiningtheradicalbiochemicalchangesatbothproteinandpost-translationmodificationlevelsduring EMT are critically important to identify regulatory factors effecting EMT andmetastatic behavior ofcarcinomas.Inthisstudy,wetakeadvantageofthecomparativeproteomicsandphosphoproteomicsmethodsthat we developed in our previous studies to comprehensively evaluate the biochemistry of a cell and itsphosphorylationeventsasitundergoesEMT.

SporadicColorectalCancerinTurkey:FromGWAStoValidation

HilalÖzdağAnkaraUniversity

ColorectalcancersshowthethirdhighestmortalityrateinWesterncountriesandTurkey.Overthepast10years, in most of the OECD countries mortality from colorectal cancer (CRC) has significantly decreased.However,CRCmortalityinTurkeyhasunfortunatelyincreased.Studiesshowedthat85-90%ofCRCcasesaresporadicwhile10-15%ofcasesarefamilialorhereditary.Todate,mostofthegenescausinghereditaryandfamilial CRC have been identified. Meanwhile, many studies have recently been conducted in order toidentify genes responsible of predisposition to sporadic CRC bymeans of genomewide association studies(GWAS).

OurgrouphasconductedthefirstfamilybasedGWASinTurkishpopulation.Withintheframeofthisstudy,primarily anovel approachpreviouslydevelopedbyour groupadapting SPRT (Sequential ProbabilityRatioTest) analysis to family based association analysis was used. SPRT contrary to TDT (TransmissionDisequilibriumTest)which requiresat least200 trios inorder toconducta familybasedassociationstudy,hastheabilitytoconductareliableassociationanalysisusinglessthan200trios.Thus,250KSNParraydataof 51 trios (sporadic colorectal cancer cases and their healthy parents)whichwere included in this familybasedGWASwere analyzed by SPRT. The analysis revealed that 75 SNPs showed genetic associationwithCRC.

Thevalidationofthis75candidateSNPswasconductedbykompetitiveallelespesificamplification(KASP)in1019sporadiccolorectalcancercasesand948healthy individualswhichwerecollected from45centres in

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Turkey.Besides these75SNPs,7SNPsassociatedwithCRC(p<1.5x10-10) inEuropeanpopulationswasalsoinvestigatedinourstudygroup.Statisticalanalysisshowedthat5SNPswhicharelocalizedin1p32.2,1q43,4p15.2,5q11.2,3q25.32areassociatedwithCRC(p<0.05).Furthermoreamong7EuropeanCRCassociatedSNPs we have found that only one, namely rs6983267, was associated with CRC (p<0.0018) in Turkishpopulation.

To the best of our knowledge this study is the first and only GWAS and its validation results in Turkishpopulation. This study has clearly showed that CRC genetic predisposition profile of Turkish population isdifferentfromEuropeanpopulations.

SystemsbiologyapproachestoconstructRNAinteractionnetworksregulatingchemotherapyresistanceandmetastasisintriplenegativebreastcancer

ÖzgürŞahinBilkentUniversity

Triplenegativebreastcancer(TNBC)isthemostaggressivetypeofbreastcancerwithhighincidencerateoflungmetastasis,andcurrenttherapiesarelimitedtochemotherapy.Notonlyproteincodingtranscripts,butalso non-coding transcriptome, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), playcritical roles in cancer progression, therapy resistance andmetastasis. To this end,we aimed to integratetranscriptomics and functional genomics to discover RNA interaction networks modulating chemotherapyresistanceandmetastasisinTNBCs.

To identify chemotherapy resistance-associatedmiRNA-mRNA regulatorynetwork in TNBCs,wedevelopeddoxorubicin resistance animalmodels in vivo and performed next generation sequencing (both RNA- andsmallRNA-seq)comparingdoxorubicinsensitiveandresistanttumors.UsingmiRNAtargetfilterandpathwayenrichmentanalysesandnetworkbiologyapproaches,weincorporateddifferentiallyexpressedmiRNAsandmRNAs into an interaction network. Later, miRNA and mRNA expression based survival analysis ofchemotherapy-treatedTNBCpatientshelped to strengthen the clinical relevanceof theproposednetworkandto findthecoremechanisms involved indoxorubicinresistance inTNBCs.Wearecurrently testingtheroleofcriticalnodestoovercomechemoresistance.

InordertoconstructthefirstmRNA-miRNA-lncRNAcompetingendogenousRNA(ceRNA)networkcontrolingmetastasis in TNBCs, we established primary tumors, and human-in-mouse (HIM) and mouse-in-mouse(MIM)lungmetastasismodelsusingTNBCcelllines.WeperformedbothRNAandsmallRNAsequencingandobtaineddifferentiallyexpressedmiRNAs,mRNAsandlncRNAsbetweenprimaryandmetastatictumors.Wethenintegratedthesethreelayersofdatawithfunctionalenrichments,pathwaymapsandtargetpredictionsto construct the ceRNA network controlling lung colonization in TNBCs. Currently, we are testing thefunctionalrolesofcandidatelncRNAsininvitroandinvivometastasisassays.

Ultimately, these studies will uncover novel non-coding RNAs and proteins that can be used as potentialtargetstoovercomeresistanceorblockmetastasis.

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Proteininteractionapproachestoassignafunctionforanestrogenresponsivegeneprotein:CXXC5

MesutMuyan

METU

Estradiol(E2),themainestrogenhormoneincirculation,is involvedinthephysiologicalregulationofmanyorgansandtissues,includingmammarytissue.E2alsoplaysacriticalroleintheinitiationanddevelopmentofbreastcancer.EffectsofE2oncellsaremediatedbyestrogenreceptor(ER),whichisaligand-dependenttranscription factor. Upon binding to E2, ER regulates the expression of target genes through genomicsignalingpathwaysthatresultinalterationsofcellularresponses.TheinteractionoftheE2-ERcomplexwithspecific DNA sequences, estrogen response elements (EREs), constitutes the initial stage of the ERE-dependentgenomic signalingpathway.The functional interactionofE2-ERwithother transcription factorsbound to their cognate response elements signifies the ERE-independent genomic signaling route. Theexpression of estrogen responsive genes mediated by E2-ER encompasses proteins involved in themetabolism of nucleic acid/proteins, transcription factors, membrane signaling cascade and receptorproteins.Theseproteins inturnparticipate intheregulationofsecondarygeneexpressionsresponsibleforDNArepair,cellcycleanddivisionand,consequently,intheinitiationofE2-mediatedcellularproliferation.

TheidentificationofE2responsivegenes,characterizationofmechanismofgeneexpressionsanddiscerningfunctions of protein products could lead to important gains towards understanding of the physiology andpathophysiologyofestrogensignaling.Ourpreviousstudies indicatedthatCXXC5isanestrogenresponsivegene. Although, the structure and function of CXXC5 are largely unknown, due to the presence of a zinc-fingerdomain-CXXCdomain (ZF-CXXC),CXXC5 ispresumed tobeamemberof theZF-CXXCprotein family.ThebindingofZF-CXXCproteinstoCpGdinucleotidesissuggestedtopreventcytosinemethylationleadingtotheformationofanucleationsiteforthedirectorindirectrecruitmentofhistonemodifyingproteinstoDNAfor transcriptionregulation.WethereforepredictedthatCXXC5,synthesizedas theprimaryresponsegeneproduct,participatesasanepigeneticfactorintheregulationofE2-mediatedcellularprocesses.

OurrecentstudiesfurtherindicatedthattheexpressionoftheestrogenresponsiveCXXC5geneismediatedbytheE2boundERthroughadirectinteractionwithanEREsequencepresentattheupstreamregionofthefirstencodingmethionine in theCXXC5gene locusthatresults inalterations inCXXC5protein levels in thenucleus of a cell model. Moreover, we found that CXXC5 is indeed a CpG dinucleotide binding protein.Furthermore, our studies using proximity biotinylation assay, BioID, coupled to LC-MS/MS revealed thatCXXC5putativelyinteractswithanumberofproteinsinvolvedinthechromatinremodeling,DNArepairandcellcycle.

Ourongoingstudiesaimedattheverificationofprotein-partnerinteractionswiththeuseofvariousinvitroand in cellula approaches would be supportive for our prediction that CXXC5 as an epigenetic factor isinvolvedinE2-mediatedcellularevents.

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SELECTEDTALKS

SESSIONI:StructuralBioinformatics

AnIntegrativeFrameworkforStructureDeterminationofMolecularMachines

EzgiKaraca*,JoãoRodrigues,AndreaGraziadei,AlexandreBonvinandTeresaCarlomagno

*EMBL&iBG-izmir{ezzgikaraca@gmail.com}

Classical structural studiesofmolecularmachinesarehamperedby technical limitationsofhigh-resolutionmethods (NMR spectroscopy and X-ray crystallography), due to the large size of these systems and theirhighly flexible nature. Luckily, even for such challenging systems, we can access low-resolution structuralinformation from complementary biochemical and biophysical experiments. Most of the time, however,these data are rather sparse and contain limited information. In order to translate such data into viablestructuralmodels,integrativecomputationaltoolsarerequired.

During the past decade, a handful of integrativemethods have been proposed. Though, asmost of themweredeveloped inacase-specificmanner,there isstillanongoingdemandforastandardizedtool.Tothisend,weproposeageneral integrative structuredetermination framework that candealwithmoleculesofdifferentnature(e.g.proteins,nucleicacids)andmakeuseofdiversedistanceand/orproximityinformationtodeterminethestructuresofmolecularmachines.

Inmytalk, Iwill first introducetheconceptof integrativemodelingandour integrativeframework.Then, Iwillillustratetheperformanceofourapproachwithtwoproof-of-principleexamples.Finally,Iwillunderpinits strengthwith a real case application, theBoxC/Dmolecularmachine,whereweuse a combinationofNMR distance information and complementary SAXS/SANS shape data to elucidate how this machineregulatesribosomalRNAmethylation.

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Analysisofsingleaminoacidvariationsinhotspotandhotregionresiduesofprotein-proteininteractioninterfaces

SılaÖzdemir*,AttilaGursoyandOzlemKeskin

KoçUniversity{eozdemir14@ku.edu.tr}

Single amino acid variations (SAVs) on protein-protein interaction (PPI) sites play critical roles in diseases.Distribution of SAVs within PPI site (interface) residues may be related to their classification as disease-causingorbenign. Interfaceshavea small subsetof residues calledhot spots that contributemore to thebinding energy, and they may form clusters called hot regions. We performed statistical and structuralanalysesofSAVswithexperimentalthermodynamicdata,anddemonstratethatSAVsdestabilizingPPIsaremore likely to be found in hot regions and hot spots rather than energetically less important interfaceresidues.Incontrast,non-hotspotresiduesaresignificantlyenrichedinneutralSAVswhichdonotaffectPPIstability. Hot spots also tend to be enriched in destabilizing and disease-causing SAVs, while neutral andbenignSAVssignificantlyoccurinnon-hotspotresidues.Also,analysisoftemperaturefactorshowedthathotspotSAVresiduesaregenerallylessflexiblecomparedtooverallSAVresiduesandhotspotresidueswithoutSAVs.FordistributionofSAVswithinsecondarystructureelements(SSEs),weobservedthatdisease-causinghotspotSAVstendtobelocatedincoil,turnandbridges,whilebenignhotspotSAVsaremorelikelytooccurinhelices.Ourworkdemonstrates thatdistributionofSAVswithindifferent interface residuesand regionscanbeimportanttopredicttheeffectofSAVs.

Interactionoffarnesylated,butlikelydepalmitoylated,RasisoformswithPDEδ

SerenaMuratcıoğlu*,HyunbumJang,AttilaGursoy,OzlemKeskinandRuthNussinov

KoçUniversity{smuratcioglu@ku.edu.tr}

PDEδshuttlestheK-Ras4Bisoformfromtheendoplasmareticulumtotheplasmamembrane.ThisisacriticalstepsinceRasneedstobemembrane-attachedthroughitshypervariableregion(HVR)torelythesignal.Thehypervariableregion(HVR)ofK-Ras4B isusually farnesylated,butcanbegeranylgeranylated.Experimentaldata indicate thatPDEδbindsK-Ras4BaswellasN-Ras;butnotK-Ras4A,which isa splicevariantofKRASgene.TheHVRsofK-Ras4BandK-Ras4Aaresimilar,botharehighlypositivelycharged.N-Ras,ontheotherhand,isnearyneutral.K-Ras4Bisfarnesylated;K-Ras4AisfarnesylatedandpalmitoylatedasisN-Ras.Unlikefarnesylation,palmitoylation is reversible.Herewe investigated thebindingofPDEδwith farnesylatedandgeranylgeranylated K-Ras4B, and analyzed the interactions.We observe that themajor contributor to theinteraction is the farnesyl/geranylgeranyl moiety through docking into PDEδ’s hydrophobic pocket.Importantly, we also observe that the flexible depalmitoylated K-Ras4A HVR can also be accommodated,corroborating with the experimental observation that depalmitoylated N-Ras can interact with PDEδ.Wethuspropose thatPDEδcanbind todepalmitoylatedK-Ras4A inoncogenic cells suchasadenocarcinomas,which are K-Ras4B cancers, and that drugs blocking the shuttling of K-Ras4B may also hinder that ofdepalmitoylatedK-Ras4A.

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SessionII:HealthInformatics

WearableSolutionsforParkinson'sDiseaseMonitoring

KorayAçıcı,ÇağatayBerkeErdaş,TunçAşuroğlu,MünireKılınçToprak,HamitErdemandHasanOğul

Parkinson'sDisease(PD)isaneurodegenerativeproblem,whichoccursduetothelackofenoughdopamineinthebrain.Whilethereisnotcurrentlyaknowncureforthedisease,thecrucialissueformedicaltreatmentof the patients with PD is constant monitoring of the disease symptoms such as tremor, bradykinesia,dyskinesiatomaintainand/orimprovetheirqualityoflife[1].SincethePDpatientsusuallysufferfromlosingbasicmotor abilities, their remotemonitoring is a recent challenge to serve a satisfactory home care andclinicalsupport.Currentcomputationalresearchonthetopichasbeenfocusedondevelopingsystemsandalgorithmsforrecognizingtheactivitiesofdailylivingusingmotionsensors[2-4].Theclinicalstudiescanonlybenefit from these computational efforts formaking binary decisions about disease state [5-7]. Here, weoffer a signal-processing-based solution for quantitatively monitoring PD motor symptoms in addition torecognizing daily living activities throughwireless on-body sensors. Specifically, our approach can provideeffectivediagnosis andprognosis solutions fromgait analysis viamultiple foot-worn sensors thatmeasureground reaction force (GRF).We argue that a regression-based analysis of GRF signals acquired from gaitsensors can accurately predict the PD symptoms in terms of Universal Parkinson Disease Rating Scale(UPDRS).Anexperimentalanalysisconductedonarealdatasetacquiredfrom73healthycontrolsand93PDpatients has justifiedour argument.Main advantageof this solution is the fact that an exact valueof thesymptom in UPDRS can be inferred rather than a categorical result that defines the severity of motordisorders.Anotheradvantage is thefact thatthemethodcanadditionallyreport theactivityperformedbythepatientwhenthecurrentsymptomanalysisisbeingcarriedout.

Acknowledgement:ThisstudywassupportedbytheScientificandTechnologicalResearchCouncilofTurkey(TUBITAK)undertheProject115E451.

References1. Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M.,Bonato,P.:MonitoringmotorfluctuationsinpatientswithParkinson'sdiseaseusingwearablesensors. IEEETransactionsonInformationTechnologyinBiomedicine13(6),864-873(2009).2. Aminian, K., Najafi, B.: Capturing human motion using body-fixed sensors: outdoor measurement andclinicalapplications.ComputerAnimation&VirtualWorlds15(2),79–94(2004).3. Erdaş Ç.B., Atasoy I., Açıcı K., and Oğul H., Integrating features for accelerometer-based activityrecognition,”ProcediaComputerScience98,522-57(2016).4. Asuroglu T., Acici K, Erdas C.B., Ogul H., Texture of Activities: Exploiting Local Binary Patterns forAccelerometerDataAnalysis,12thInternationalConferenceonSignal-ImageTechnologyandInternet-BasedSystems,Naples(2016).5.Daliri,M.R.:Chi-squaredistancekernelof thegaits for thediagnosisofParkinson’sdisease.BiomedicalSignalProcessingandControl8(1),66–70(2013).6.Jane,Y.N.,Nehemiah,H.K.,Arputharaj,K.:AQ-backpropagatedtimedelayneuralnetworkfordiagnosingseverityofgaitdisturbancesinParkinson’sdisease.JournalofBiomedicalInformatics60,169–176(2016).7.Ertugrul,O.F.,Kaya,Y.,Tekin,R.,Almali,M.N.:DetectionofParkinson’sdiseasebyShiftedOneDimensionalLocalBinaryPatternsfromgait.ExpertSystemswithApplications56,156-163(2016).

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AComputerAidedDiagnosisSystemforHeartAttackUsingDecisionTree

İlknurBUÇANKIRKBİR1,BurçinKURT1,KemalTURHAN1

1DepartmentofBioistatisticalandMedicalInformatics,KaradenizTechnicalUniversity,Trabzon,Turkey

ilknurbucan@gmail.com,burcinnkurt@gmail.com,kturhan.tr@gmail.com

Purpose-Myocardialinfarction(MI)whichisgenerallynamedasheartattackthatoccursasaresultofthecloggingofbloodvessels.Inthiscase,theheartmusclecellswhichcannotgetenoughoxygenbecauseofnotgettingenoughblood,damagemayoccurandifheartmusclestaywithoutoxygenforalongtime,deathmayoccur.50%ofdeathswhicharefromtheheartattackhappeninfirstonehourandthisratioswellsto80%infirsttwentyfourhours.Durationoftreatmentandtransportationofpatienttothehospitalplayabigroleindeaths which are from the heart attack. Therefore, a computer aided diagnosis (CAD) system has beendevelopedtohelpdoctorsinthediagnosisoftheheartattackinthisstudy.

Methods- Inthescopeofthestudy,datasof350patientswhichhadappliedwithchestpaincomplaintatFarabihospitalemergencyserviceofmedicinefacultyofKaradenizTechnicalUnıversity in2014-2016,werediagnosed and non-diagnosed with heart attack, have been used. These datas have been obtained byexaminingresultsofthebiochemistrylaboratorytest,epicrisisreportsandresultsoftheangiographyreportsin consultancy with specialist physician. Gender, creatine kinase-MB (CK-MB), high sensitive troponin I,changeofSTsegmentandchangeofECGparametershavebeenusedasinputs.ForthedevelopmentofCADsystem,thedecisiontreemodelwhichisaoneofthemachinelearningmethods,hasbeenused.InthisCADmodel,205ofthe350datausedastrainingsetandtheremaining145wereusedastestset.

FindingsandResults-Consequently,aquitesuccessfulCADsystemforheartattackhasbeendevelopedwith91.7%sensitivity,97.7%specificity.Inadditiontothis,AUCvalueof0.94andKappavalueof0.86havebeenobtainedwhichareverysatisfactoryaccordingtothesimilarstudiesintheliterature.

Keywords–Heartattack,computeraiddiagnosis,decisiontree.

CardiovascularModellingforAbdominalAorticAneurysms

EmrahAkkoyun*,AybarCanAcar,ByronZambranoandSeungikBaek

MiddleEastTechnicalUniversity{emrah.akkoyun@metu.edu.tr}

Anaorticaneurysmisdiagnosedwhenthereisalocalexpansionoftheaorta,carryingbloodcomingfromtheheart.Whentheexpandedportionis50%largerthanthenormalvesseldiameter(30mmormore)thelesionisdefinedasananeurysm.Althoughaneurysmsare seen indifferent regionsof theaorta, theycommonlypresentintheabdominalregion.Mostsmallaneurysmshavenosymptomandwereconsideredsafe,whilelargeaneurysmsmaybe fatal in thecaseof rupture,whichcausesmassive internalbleeding.This requiresimmediate interventionand, even then, generally resultsdeath (a90%mortality rate). It is the13thmostcommonkiller in theU.S. (Wilminketal,1998;Ernstetal,1993).Aneurysmrepairprior to rupture is thusvital.Studiesshowthatboththelongtermmonitoringofpatientspriortoanysurgicalintervention;aswellas the EVAR intervention required, if surgery is selected, has their own risks. Therefore, physicians arerequiredtoassessbothriskscarefullytoselectappropriatewayforward.Thestateoftheartistomakethisdecisionbasedonthediameteroftheaneurysmorannualaneurysmgrowth.Scientificresearchhasshownthatthecriterion isnotaccurateforpredictinganeurysmrisk (Vorpetal,2007;Limetetal.1991)and it isnecessarytoconsiderotherparametersforassessingrisk.Furthermore,visualizingthe3Dmodelwithacolor

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mapisveryusefultosurgeonsbeforetheoperation.Inthisstudy,biomechanicalbehavioroftheaneurysmisanalyzedinadetailedwaywithinthecontextofhemodynamicforcessuchaswallshearstressandvelocitypatterninordertobetterunderstandthereasonsforgradualaneurysmgrowthandpotentialrupture.

Thereisaconsiderableamountofliteratureonriskassessmentofcardiovasculardiseasesaswellasfortheircomputationalmodeling. Furthermore, a numberof open-source software tools are available for differentpurposes such as medical image enhancement, automatic and manual segmentation of arteries/veins,creating a proper vascular model, mesh generation and applying computational flow analyses. Hence, aconstructed computational model, based on patient’s scan images (e.g., CT and ultrasound imaging), canvisualizethestatusoflesions,andbiomechanicalsimulations(e.g.,bloodvelocity)canbeinformedtotrainedphysicians and aid them for decision-making. We present an end-to-end procedure that can be used toconstruct 3D models of the aneurysm and run hemodynamics simulations with realistic choices for flowparametersandprofiles.Ifnotdonesystematicallyandcarefully,suchsimulationsareusuallynotconsistentwithrealityand/ortakeunnecessarylongtimestocompute.

Todemonstrate, threepatientseach from the rupturedandnon-rupturedaneurysmgroups (e.g., total sixpatients)havebeenselectedandthesimulationswereperformedseparatelywithourprocedure.Theresultsforeachgroupwereobtained,comparedwiththereferencevaluesandverified.WeobservedhighWSSontherupturedaneurysmgroup,asexpected,withtheexceptionofonepatient.Figure1showsanexampleofWSSandvelocityvisualizationforbothgroupsduringthesystolicphaseatthesecondcardiaccycle.Theleftaneurysmbelongstonon-rupturedgroupwhiletherightonebelongstorupturedgroup.

More closely, when we investigate the wall shear stress distribution over the maximum values during acardiac cycle (ruptured: 6,62 and un-ruptured: 2,31), we can find a single threshold value where we candiscriminatetwoclasses fromeachotherwithoutanyexception.Therearesignificantdifferencesbetweenthegroupson these featuresevenall theotherhemodynamic forces investigatedareclose toeachother.Therefore,we are hypothesizing that distributions ofwall shear stress during a cardiac cyclemight be anindicatorofthepredictinggrowthrateandspeedofapproachtorupture.Nevertheless,thehypothesiswillbetestedfurtherinourongoingresearch,withlargerdatasets.

Insummary,inthissubmissionwedemonstratehowtoperformcardiovascularmodelingfromrawCTimagesto3Dmodeloftheaneurysmwherethecolormapshowsthedistributionofthehemodynamicforcesovertheaneurysmsurface.Theapproachcanfurtherbegeneralizedforuseinsimilarproblemsincardiovascularresearchinvolvingcoronary,cerebralandcarotidarteries.

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SessionIII:CancerSystemsBiology

Alternativepolyadenylationpatternsforcancerclassification

OguzhanBegik,MerveOyken,TunaCinkilliAlican,TolgaCan*andAyseElifErsonBensan

*DepartmentofComputerEngineering{tcan@ceng.metu.edu.tr}

Certainaspectsofdiagnosis,prognosisandtreatmentofcancerpatientsarestillimportantchallengestobeaddressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), ahiddencomplexityincancertranscriptomes,tofurtheraccelerateeffortstodiscovernovelcancergenesandpathways.Weanalyzedexpressiondata for1,045 cancerpatients and founda significant shift inusageofpoly(A) signals in common tumor types (breast, colon, lung, prostate, gastric and ovarian) compared tonormal tissues. Using machine-learning techniques, we further defined specific subsets of APA events toefficientlyclassifycancertypes.Overall,ourstudyoffersacomputationalapproachforuseofAPAinnovelgene discovery and classification in common tumor types, with important implications in basic research,biomarkerdiscoveryandprecisionmedicineapproaches.

IntegratingMultipleDataTypesforCancerSubtypeDiscovery

TundeAderinwaleandHilalKazan*

AntalyaInternationalUniversity{hilalkazan@gmail.com}

Cancerisaheterogeneousdiseaseandidentificationofcancersubtypesiscriticalforpersonalizedtreatmentanddrugdevelopment.Recently,cancergenomeprojectshaveproducedmultipletypesofhigh-throughputdata for thousands of cancer patients. Exploiting the complimentary information between different datatypescanimprovefindingsubtypes.Here,weproposeanewmethodthatintegratesmultipledatatypesforcancer subtype discovery.We focus on five cancer types: Breast Invasive Cancer, Colon adenocarcinoma,Glioblastoma multiform , Kidney renal clear cell carcinoma and Lung squamous cell carcinoma. We usegenome-widemeasurementsofgeneexpression,DNAmethylationandmiRNAexpressionofcancerpatientsfrom The Cancer Genome Atlas (TCGA) database. An intermediate integration method was used bytransformingeach typeof dataset into a radial basis function kernel andusingmultiple kernel learning toinfertheoptimalweightsforkernelcombination.Combiningkernelswithlearnedweightsgiveclusterswithhigher silhouette score compared to combining the kernels uniformly. Moreover, Kaplan-Meier analysisshowsthatourdiscoveredclustershavedistinctsurvivalprofileswithstatisticallysignificantlog-ranktestp-values.

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IdentificationofGeneMutationsInvolvedinDrugResistanceinLiverCancerUsingRna-SeqDataAnalysis

MonaShojaei,EceAkhan,AybarCanAcarandRengülÇetinAtalay

Cancer is the leading causeof deathworldwide and the risk increaseswith aging.A significant concern incancerresearchisthedetectionofcancerdrugresistanceassociatedsomaticmutations.AccordingtoGlobalCancerStatistics(GCS)livercanceristhe5thmostcommonand2nddeadliestcancerintheworld.Overthepast two decades, the death rate for liver cancer increased by 2.5% per year and incidentswere 3 timeshigherinmenthaninwomen(AmericanCancerSociety).Appropriatetreatmentofhepatocellularcarcinoma(HCC, primary liver cancer) depends on the disease stage, patient’s age and overall health and individualpriorities.Targetedtherapiesareknowntoblockcancer-associatedproteinsorpreventcellproliferationandinvasion. Unlike conventional chemotherapy, which affects the whole normal and cancerous fast-growingcells,targeteddrugsattackspecificmoleculesincancercellsandhavemuchlessimpactonhealthytissues.Withtargetedtherapyinmind, inthisstudy,therelationshipbetweenmutationstatusanddrugtreatmentresponseofwell-differentiatedHuh7andpoorly-differentiatedMahlavulivercancercellswereanalyzed.TheRNA-Seq data of each cancer cell line (as control) was compared to “sorafenib” and “PI3K/Akt Pathwayinhibitors” treated samples. Somatic mutations associated with drug resistance were comparativelyidentifiedwithMuTecttool(Cibulskis,2013).

The results were then filtered to distinguish the missense mutations. The common genes among drug-resistant sets were found to be associated with liver cancer perseverance and aggressiveness. SLC39A5,FRG1,PPHLN1andSRP9genemutationswere found tobe themost significant, sharedamong threedrugtreated sets. The sets were further investigated in detail to discover the liver cancer associated survivalgenes.Usingourresults,appropriatetargetscanbedefinedthatplaycriticalrolesincancerouscellgrowthfordrugdevelopmentpurposes.Drugswithspecifictargetscanfindtheappointedplaceandturnthetargetgeneofftodisturbthecancercells’proliferation.Accordingly,themutatedgenesactivitiesarestoppedandthediseaseprogressionisprevented.

Themutatedgenesthatweidentifiedduringthechemicalknockdownstudiescanbefurtherstudiedingeneexpression vs. patient survival data. These genes are being analyzed in laboratory to test whether theirsilence (knocking-down founded gene mutations which are correlated with liver cancer under drugtreatments) decreases cancer growth or not. In our success we can target these genes in future cancertreatments.

Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancersamples.NatBiotechnology(2013).doi:10.1038/nbt.2514

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SessionIV:Genomics

LosslessPruningofAHPSNPPrioritizatonTreeUsingRandomForestVariableImportances

ArifYılmazandYeşimAydınSon*

METUInformaticsInstitute{yesim@metu.edu.tr}

SubjectivityisanoldyetunsolvedprobleminMultipleCriteriaDecisionMakingincludingAnalyticHierarchyProcessing (AHP). Here,we have proposed amachine learning based analytic hierarchy process (ML-AHP)method to address expert judgment uncertainty in decision making system design. It is accomplished bytraining a classifier algorithmaccording toAnalyticHierarchyProcess inputdata andevaluating categoriesbased on variable importance values. As a comparative case study Single Nucleotide Polymorphism(SNP)prioritization in the bioinformatics domain is presented. Variable Importance figures provided by theemployedmachinelearnerareusedtoevaluatetheimportanceofAHPcategories.InthisstudyweselectedRandom Forest. It was discovered that most of the expert defined weights of categories were zero andperformancewas identicalafterpruning.AnalysisontheProstateCancerandtheType2DiabetesMellitusdisease data were performed to demonstrate the benefits of the proposed approach, where pairwisecomparisonsofcategoriesrequirednoexpertevaluation.Hence,subjectivity,uncertaintyandimprecisionisavoided. Implementationof theproposedmethodcanenhanceevaluationof the categoryweights inAHPandalsoinothermultiplecriteriadecisionmakingmethods.

DiscoveringLargeGenomicInversionsUsingLong-rangeInformation

FatihKaraoglanoglu*,MarziehEslamiRasekhandCanAlkan

BilkentUniversity{fkaraoglanoglu@bilkent.edu.tr}

Nextgenerationsequencing(NGS)hasbeenprovidingevercheapermeansforobtaininglargeamountsof genomic sequencing data over the last decade. These data have been utilized to call a varietygenome-levelvariations.MostwidelyusedformofNGSdatatypicallyconsistofpaired-endshortreads(~100-150bp),withlowsequencingerrorrate.Thislowerrorrateallowsforrelativelyeasydiscoveryofsmall genomic variations. However the discovery of larger variations (>50 bp), known as structuralvariations (SV), is a much harder problem. This is due to the fact that most of short reads wouldcompletelyliewithinanSVregion,andthusprovidenoinformationabouttheexistenceofsaidSV.Evenwhen short reads capture SVsbreakpoints, correctlymapping these short reads isnot trivial since SVbreakpointstypicallylieinduplication-orrepeat-richregions.

We present VALOR (VAriation using LOng Range information), an algorithm for discovering largeinversions. VALOR utilizes the linked-read sequencing data generated using the new 10X Genomicsplatform,which tagspaired-end short-reads thatoriginate from the same largeDNAmoleculewith aunique barcode. VALOR uses this long-range information to discover large inversions. It is alsostraightforwardtoextendVALORforthediscoveryofotherformsoflargeSV,andincorporatesplitreadandlocalassemblysequencesignaturestoimproveaccuracyandbreakpointestimates.

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Neighbouringsequencecontext-basedfixationbiasofmutationsinchimpanzeegenome

ElifBozlak*,YetkinAlıcı,EvrimFer,MelikeDönertaş,RasmusNielsenandMehmetSomel

InformaticsInstitute,METU{bozlakelif@gmail.com}

The rate at which different types of mutations occur in genome, and the rate they spread throughoutpopulationsandfix,canvarydramatically.Forexample,transitionsoccurmorefrequentlythantransversions,andCtoTmutationsatCpGsitesarethemostcommonmutationtypeinmammalians.Inadditiontothesemutationratebiases,GC-biasedgeneconversionisawellknownfixationbias,suchthatmutationsfromA/TtoG/Cinrecombinationhotspotscanspreadthroughoutpopulationsandconsequentlygetfixedathigher-than-neutralrates.Previouslywestudiedpossiblefixationbiasesinthehumangenometakingneighbouringsequence context into account, analysing 5bp sequences with themutated base in themiddle.We havefoundthatmutationsthatextendthesamebasesequences(e.g.AGCGG->AGGGG)arefixedmorerapidlyincomparisontomutationsthatdisruptsuchsequences(e.g.AGGGG->AGCGG).Thechimpanzeeisthemostclosely relatedspecies tohuman,andhas thebeststudiedgenomeamongprimates,besideshuman.Herewe study whether this fixation bias exists also in the chimpanzee lineage. Using published chimpanzeepopulation genomic data from 75 individuals, and comparative genomic data of chimpanzee-human-orangutangenomealignment,wedetermineancestral stateof chimpanzeebases throughoutgenomeandclassifymutationsonthatlineageaspolymorphicorfixed.UsingtheMcDonald-Kreitmantestwethencheckforafixationbiasofallsinglenucleotidemutationsintheir5mersequencecontext,relativetotheirreversemutations.

SessionV:Multi-omics

Identificationofdysregulatedpathwaysacrossmultiplecancertypes

CesimErten*,EvisHoxha,HilalKazanandEsraTepe

AntalyaInternationalUniversity{cesimerten@gmail.com}

A major challenge in cancer genomics is to distinguish driver mutations that are responsible for cancerdevelopmentfrompassengeralterationsthatareobservedduetochance.Recentstudiesshowthatthereisa limited overlap between the driver mutations across multiple samples of the same cancer type. Thismutationalheterogeneityisexplainedbythefactthatdrivermutationscantargetmultiplegenesinthesamefunctionalpathway.Also,alterationsof thealternativedrivergenes in the samepathwayexhibitamutualexclusivitypatternduetoreducedselectivepressure.

Inthisstudy,weproposeamethodthataimstoidentifydrivereventsofcancerbyconsideringfunctionallyrelatedgeneswithmutationprofilessubjecttotheabove-mentionedprinciples.OurmethodisbasedontheexistingHotnet2algorithm[1].Hotnet2takesasinputahumanPPInetwork,appliesarandomwalkstrategyimplementingaheatdiffusionprocesswhereheatscorrespondtomutationfrequenciesacross3110samplesfrom12cancertypes.Sucharandomwalkprovidesthesocalled‘exchangedheatmatrix’whereanentry(p,q)correspondstotheextentofthe influenceonqofthemutationsoccurringatp.Oncethe lowinfluencepairs are removed from consideration, the remaining “exchanged heatmatrix” corresponds to a directedgraphfromwhichstronglyconnectedcomponentsareidentifiedasoutputmodules.

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We extended the existing Hotnet2 algorithm to incorporate mutual exclusion and coverage. Namely, weconstructed an edgeweighted PPI networkwhere each edgeweight between a pair of genes reflects thedegreeofmutual exclusionand the coverageof thepair across all the samples.Wemodified the randomwalksothattheedgeweightsaretakenintoconsiderationappropriately.TherestofthestepsarethesameasHotnet2 algorithm.When the total numberof genes in theoutputmodules is fixed, ourmodules havehigherscoresforconnectivity,mutualexclusionandcoveragecomparedtoHotnet2-identifiedmodules.ThenumberofknowncancerdrivergenesselectedbetweenourmethodandHotnet2iscomparable.Moreover,thesetofmodulesprovidedbyourmethodhasbetterGOconsistencyscoresthanthatofHotnet2.

In summary, ourmethod combines the knowledge in theexisting interactionnetworkwith cancer-specificprinciples (i.e., mutual exclusivity, coverage) to identify driver pathways. The identified modules will beinstrumentalinprovidingdirectionsfornewdiagnosticandtherapeuticstrategiesincancerbiology.

[1] Leiserson et al, Pan-cancer network analysis identifies combinations of rare somatic mutations acrosspathwaysandproteincomplexes.NatureGenetics,47(2):108(2015).

[2]Vogelstein,Betal.Cancergenomelandscapes.Science,339,1456(2013).

Integrativebioinformaticsanalysisofmulti-omicsdatafacilitatesfunctionalcharacterizationofthecandidatetumorsuppressorproteinCTCF

GuvanchOvezmyradov

Dept.ofBiostatisticsandMedicalInformatics,RegenerativeandRestorativeMedicineResearchCenter(REMER),IstanbulMedipolUniversity{guvanch@medipol.edu.tr}

TheCCCTC-bindingfactor(CTCF)isamultifunctionalzinc-fingerprotein,knownas“themasterweaverofthegenome” for its unique role in coordinating the 3D organization of the genome and regulating geneexpression.CTCFproteinactsasatranscriptionfactorandoccupiesamyriadoftargetsitesacrossthehumangenome. Considering the genome-wide significance of CTCF, is this regulatory protein also implicated incancer? The answer remains elusive, partly becauseof the versatile natureof this protein and the lackofexperimental studies addressing this question. Current evidence only loosely links CTCF to cancer as acandidatetumorsuppressorgeneandmoreworkisneededtoclarifytheCTCF'sinvolvementincancer.Vastdata generated by numerous experimental studies, which is available in multiple databases, offersunprecedentedpossibilitiestoaddressthis issue.Inthisstudy,multipletypesofomicsdataareanalyzedinthe context of CTCF and cancer using dataminingmethods. The integrative bioinformatics approach linksCTCF-associated transcriptomics, interactomics and functional annotation data by reconstructing andanalyzing the CTCF protein-protein interaction network. The resulting putative CTCF interaction mapimplicates CTCF with distinct biological processes and co-expression pattern of CTCF interaction partnersprovidesindicationsabouttheunderlyingcellularmechanisms.Preliminaryresultsfromthismeta-analysisofmulti-omicsdatacomplementsexperimentalresearchfindingsandprovidenovelinsightsintoimplicationofthiscandidatetumorsuppressorproteinincancer.

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SessionVI:ComputationalDrugDiscovery

RDFGraphEmbeddingsforPredictionofDrug-DrugInteractions

RemziÇelebi,ErkanYaşar,ÖzgürGümüşandOğuzDikenelli

EgeUniversityComputerEng.Dept.{remzi.celebi@ege.edu.tr}

Linked Open Data (LOD) is a technique for publishing, describing, and linking data. Linked open data is apotential sourceofbackgroundknowledge formodelingpredictivemachine learningandbuildingcontent-based recommender systems. LOD identify resourceswithUniformResource Identifiers (URI) and throughstandardssuchastheRDF(ResourceDescriptionFramework).RDFisapowerfuldatamodeltodescribeandexchangeresourcesontheWeb.Bio2RDFisanopen-sourceprojectthat integratesnumerousLifeSciencesdatabases available on different websites. Bio2RDF provides a service for data integration service fromvarioussourcesasaresourceforscientificresearchers.Bio2RDFscriptsconvertheterogeneouslyformatteddatasuchasSQLandXMLintoRDFformat.Bio2RDFcreatesalargeRDFgraphthatinterlinksdatafrommajorbiologicaldatabases,includingDrugbank,KEGGandSIDER.

RDF2VecisarecentlypublishedmethodologythatadaptsthelanguagemodelingapproachofWord2VectoRDFGraphEmbeddings.Word2Vectrainsaneuralnetworkmodelto learnvectorrepresentationofwords,calledwordembeddings.Itmapseachwordtoavectoroflatentnumericalvaluesinwhichsemanticallyandsyntactically closer words will appear closer in the vector space. The hypothesis which underlies thisapproachisthatcloserwordsinwordsequencearestatisticallymoredependent.RDF2VecappliesasimilarapproachtoRDFGraphconsideringtheentitiesandrelationsbetweenentitiesbyconvertingthegraphintoset of sequences of entities (walks or paths) and trains the same neural network model to learn vectorrepresentationofentitiesintheRDFgraph.

Drug-drug interactions (DDIs) are a very important topic in drug discovery and public health. Drug-druginteraction(DDI)mayoccurwhenmultipledrugsareco-prescribed,andtheseinteractionshavethepotentialtoleadtopatientdeathordrugwithdrawal,apotentialwhichmakesittheobjectofgreatinterestfrombothacademia and industry. Prediction of potential drug drug interaction helps reduce unanticipated druginteractions, drug development costs and optimizes the treatments in the drug design. The underlyingassumption of the similarity based DDI prediction approach is that similar drugs may interact with samedrugs. There are already several similarity-based approaches, the most common of these are structural,therapeutic, phenotypic and genomic similarities [1]. The studyby Zhang,Wen, et al. [2] collected awidevarietyofdrugdataandthuspredicteddrug-druginteractionsbyintegratingchemical,biological,phenotypicandnetworkdata.Gottliebetal. [3]builtanovel framework, INDI,whichconsideredsevenkindsofdrug-drug similarities. The Villar et al. study [4] designed a novelmolecular fingerprint similarity based on DDIprofilesanddevelopedausefulinsilicomodeltopredictnewdruginteractions.Chengetal.[5]presentedaHNAI framework to predict drug interactions utilizing the drug phenotypic, therapeutic, structural, andgenomic similarities. They applied five machine-learning-based predictive models (decision tree, logisticregression,naiveBayes,k-nearestneighbor,supportvectormachine)onadrug-druginteractiondataset.

In thiswork,we have applied RDF2Vec to extract feature vector representation of linked open drug datafromsubsetofBio2RDFdatasetstopredictpotentialdrug-druginteractions.WegeneratedwalkstobeusedasinputforvectorrepresentationofdrugsonRDFgraphdata.Asimilarityorrelatednessscorebetweenanytwodrugscouldbecalculatedbytakingthecosineofthosedrugvectorrepresentations.Weextendedourprevious work [1] by integrating the RDF Graph Embedding based drug similarities to train a logistic

30

regressionclassifierforDDIprediction.Ourpreliminaryresultssuggestthatdrugvectorrepresentationbasedsimilaritiescouldenhanceexistingpharmacologicalsimilarity-basedDDIpredictionmethods.TheAUCvaluehasbeenincreasedfrom0.67to0.76basedonfivefoldcross-validationwiththesenewsimilarities.

References

1- Çelebi, R.,Mostafapour, V., Yasar, E., Gümüs, Ö., & Dikenelli, O. "Prediction of Drug-Drug InteractionsUsingPharmacological SimilaritiesofDrugs."DatabaseandExpert SystemsApplications (DEXA), 201526thInternationalWorkshopon.IEEE,2015.

2-Zhang,W.,Chen,Y.,Liu,F.,Luo,F.,Tian,G.,&Li,X.(2017).Predictingpotentialdrug-druginteractionsbyintegratingchemical,biological,phenotypicandnetworkdata.BMCbioinformatics,18(1),18.

3-Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R. INDI: a computational framework for inferring druginteractionsandtheirassociatedrecommendations.MolSystBiol.2012;8:592.

4-Vilar,S.,Uriarte,E.,Santana,L.,Tatonetti,N.P.,&Friedman,C.(2013).Detectionofdrug-druginteractionsbymodelinginteractionprofilefingerprints.PloSone,8(3),e58321.

5-Cheng F, Zhao Z. Machine learning-based prediction of drug-drug interactions by integrating drugphenotypic,therapeutic,chemical,andgenomicproperties.JAmMedInformAssoc.2014;21(e2):e278–86.

DrugRepurposingforAgeing:AConnectivityMapApproach

HandanMelikeDönertaşandJanetThornton

EMBL-EBI{donertas.melike@gmail.com}

Ageing is broadly defined as time-dependent functional decline and loss of physiological integrity inbiological systems leading to reduced homeostasis and susceptibility to many pathologies includingneurodegenerative disorders. Genetic experiments on model organisms proved the possibility oflifespanextensionupto10-fold.Whilenotaspowerfulasthegenetic interventions,severaldrugsarealso tested in model organisms and shown to modulate lifespan or improve health during ageing.Identification of new drug candidates, however, is not straightforward mainly due to molecularmechanismsofageingprocessbeingmostlyuncovered.

Inthisstudy,wehaveusedadrugrepurposingapproach,i.e.applicationofexistingdrugstotargetnewbiologicalprocesses,allowingidentificationofdrugstargetingthesamepathwaysthatchangeinageing.Usingmultipleage-seriesgeneexpressiondatasets(microarrayandRNA-Seq)thatareavailableinpublicdatabases,weidentifiedgenesshowingaconsistentchangewithageinginhumanbrain.Wecomparedtheir expression changes during ageing with the drug perturbed gene expression changes. Using theConnectivityMapdatabase,whichincludesdrugperturbationexperimentsfor1309drugs,weidentifiedasetofdrugswhichcanpotentiallymodulateageingprocessandextend lifespan.Thetopcandidateswe identified include drugs such as rapamycin, which are known to modulate lifespan in modelorganisms.Theapproachhasadvantagesthatitstartswithhumanageingdata,increasingthepossibilitythat these drugs can also be functional in humans and it is not biased toward previously identifiedageingrelatedpathways,makingitpossibletodiscovernewproteintargetsforageingstudies.

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SessionVII:ProteinFunctionPrediction

InvestigationofMulti-taskDeepNeuralNetworksinAutomatedProteinFunctionPrediction

AhmetSureyyaRifaioglu1,*,MariaJesusMartin2,RengülÇetin-Atalay3andMehmetVolkanAtalay1,TuncaDoğan2,3

1DepartmentofComputerEngineering,MiddleEastTechnicalUniversity,Ankara,Turkey,2EuropeanBioinformaticsInstitute(EMBL-EBI),Hinxton,Cambridge,CB101SD,UK,3CanSyL,GraduateSchoolof

Informatics,MiddleEastTechnicalUniversity,Ankara,06800,Turkey*Towhomcorrespondenceshouldbeaddressed:arifaioglu@ceng.metu.edu.tr

INTRODUCTION

Functional annotation of proteins is an active research field for understanding molecular mechanisms ofliving-beingsandalsoforbiomedicalpurposes.SeveralGeneOntology(GO)basedproteinfunctionpredictionmethodshavebeenproposed inthe lastdecade.However,consideringthepredictionperformancesoftheproposedmethods,itcanbestatedthatthereisstillroomforsignificantimprovementsinthisarea(1).Deeplearningtechniquesbecamepopularinrecentyearsandtheyturnedouttobeanindustrystandardinseveralareas such as computer vision and speech recognition. To the best of our knowledge, as of today, deeplearningalgorithmshavenotbeenappliedtothelarge-scaleproteinfunctionpredictionproblem.Here,wepropose a hierarchical multi-task deep neural network architecture, DEEPred, as a solution to proteinfunctionpredictionproblem.Firstofall,weinvestigatedthepotentialofemployingdeeplearningmethodsforproteinfunctionprediction.Forthispurpose,wemeasuredtheperformanceofourmodelsatdifferentparametersettings.Furthermore,weexaminedtherelationshipbetweentheperformanceofthesystemandthesizeofthetrainingdatasets.

METHODS

For training, we created a dataset using GO term annotations from UniProtKB/SwissProt with manualexperimentalevidencecodes.WedividedthisdatasetaccordingtodifferentlevelsinGOhierarchy(i.e.9to12 levelswith respect todifferentGOcategories).Theobjectivehere is tocreateamulti-taskdeepneuralnetwork model for GO terms at each level of a GO category. The models were trained with drop-outtechniquetoavoidoverfitting.Forthegenerationofproteinfeaturevectorsattheinput,weusedamodifiedversionofsubsequence-basedfeatureextractionmethodcalledSPMap(2).Ageneralviewofthemethodisshown in Figure 1A. In the proposedmethod, a task corresponds to a GO term, therefore,when a querysequence is fed to ourmodels as input, DEEPred calculates a score for each trained GO termwithin themodel (Figure 1B).We determined a threshold value for each GO termwithin a level to provide reliablepredictions.

Level specificmodels consider the GO termswithin a level of GO directed acyclic graph and they do notconsider the GO terms between different levels. Therefore, we used a method to provide predictionsconsideringalllevelsofGOhierarchy.Inthismethod,inordertoprovideapositiveGOtermpredictionforaqueryprotein,we also check theprediction scores of its parents.Wepresent the correspondingGO termprediction, if the target GO term andmajorıty of its parents are over the predetermined thresholds.Wecreatedseveralmulti-taskfeed-forwarddeepneuralnetworkmodelswithseveralparametersfornumberofhiddenlayers,numberofneurons,learningrateanddrop-outrateandusedthebestperformingmodelsforeachlevel.

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RESULTS

WecreatedsixtrainingdatasetsforeachGOcategorywhereeachdatasetcorrespondstoGOtermshavingprotein associations with numbers greater than the specified thresholds; (i.e. > 50,100,200, 300,400,500annotatedproteins).Alevelspecificevaluationwasperformedforperformancecalculations.Here,ourfirstobjectivewas to demonstrate the relation between the GO levels (e.g. generic/specific) and classificationperformance. The second aim was to show the relation between performance and size of the trainingdatasetsthatwereusedforeachGOterm.Theresultsshowedthatthereisageneraltrendofperformanceincreasewith the increasing number of training sampleswhichmeans that includingGO termswith smallnumberofproteinassociationsdecreasestheperformance(Table1).However,weobservedthatthereisnocorrelation between GO levels and performance. Overall system performancewas evaluated byMathewsCorrelationCoefficient(Table1).Theresultsshowedthatthesystemperformancewassatisfactoryanddeeplearning can be employed to improve the performance for hard to predict GO categories such as thebiologicalprocessandthecellularcomponent.Asaconclusion,weshowedthatdeeplearningtechniqueshasa significant potential in protein function prediction. We plan to further optimize the models to provideDEEPredasanopen-accesstooltotheresearchcommunity.

FIGURE

Figure1:OverviewofDEEPredarchitecture:(A)Trainingdatasetandmodelconstruction;(B)Modeltrainingforalevel

Table1:Performanceresultsforlevelspecificandoverallsystemevaluation.

LevelSpecificPerformance(F-Score) OverallPerformance

(MathewsCorrelationCoefficient)50 100 200 300 400 500MolecularFunction 0.69 0.72 0.71 0.76 0.82 0.82 0.75

BiologicalProcess 0.42 0.42 0.47 0.47 0.50 0.51 0.49

CellularComponent 0.63 0.65 0.66 0.69 0.70 0.73 0.63

REFERENCES

1.Jiang,Y.,Oron,T.R.etal.(2016).Anexpandedevaluationofproteinfunctionpredictionmethodsshowsanimprovementinaccuracy.Genomebiology,17(1),184.

2.Saraç,Ö.S.,Atalay,V.,andCetin-Atalay,R.2010GOPred:GOmolecularfunctionpredictionbycombinedclassifiers.PLoSONE,8pp.1:11

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ASelf-trainingApproachforFunctionalAnnotationofUniProtKBProteins

RabieSaidi*,MaryamAbdollahyanandMariaMartinUniProt,EuropeanBioinformaticsInstitute,Cambridge{rsaidi@ebi.ac.uk}

1.INTRODUCTIONAutomaticannotationsystemsareessentialtoreducethegapbetweentheamountofproteinsequencedataand functional information in public databases such as UniProtKB (1). These systems rely on manuallyannotated (also called labelled) data to learn rules for predicting annotations.Manually labelled data are,however,oftenscarceortimeconsumingtoobtainastheyhavetobereviewedbyexperthumancurators.Ontheotherhand,unlabelleddataareabundantandcomparativelyeasytogather.Inthiswork,wepresenta self-training (2) automatic annotation approach that utilises unlabelled data in order to improve theaccuracy of predictions.Weevaluatedour systemon a set of entries inUniProtKB/Swiss-Prot. The resultsshow improvement indifferentperformancemetricswhen self-training isused. ThegeneratedmodelwasthenusedtopredictmetabolicpathwayinvolvementofUniProtKB/TrEMBLproteins.Asaresult, itcovered86%of theproteins currentlyannotatedbyUniProtpipelines,butalsocouldannotate6.7millionproteinsthatlackedanypreviouspathwayannotations.2.MOTIVATIONANDMETHODSIn a previous work, we introduced the Association-Rule-Based Annotator (ARBA), a multiclass annotationsystem for automatic classification and annotation of UniProtKB proteins. The system was evaluated onUniProtKB/Swiss-Prot prokaryotic data where it achieved very promising results (3). However, 89.7% OfUniProtKB/Swiss-Prot entries have been automatically annotated using HAMAP annotation rules (4). Thisleadstothefollowingquestion:canARBAstillpredictannotationsintheabsenceofannotationsprovidedbyHAMAPandusingonlymanualannotations?Toanswerthisquestion,weconsideredthetaskofpredictingmetabolicpathways inUniProtKBbacterialdatausing twodifferentapproaches: first, theoriginalARBAasdefinedin(3)andsecond,ARBAcombinedwithself-trainingasintroducedbelow.In order to dealwith small amount of labelled data, ARBAwas self-trainedonUniProtKB/Swiss-Prot data.Proteins that containpathwayannotations constitute the labelleddatasetwhile those thatdonot containany pathway annotation constitute the unlabelled dataset. The systemperforms self-training in twomainsteps. In the first step, annotations arepropagated from the labelled to theunlabelled setbasedon theirsimilarity. The similarity criterion is defined as a Boolean that is true if the two proteins have the sameattributes; e.g., signatures. In the second step, ARBA iteratively learns from these data, retrains itself andaddstothelabelledinstancesuntiladesiredperformancelevelisreached.Theoutputofself-trainingisthefinal learningdatasetusingwhich theannotationmodelwasbuilt. Thismodelwas validated in two2-foldcross-validationruns.Theresults,averagedoverthetworuns,areshowninTable1.Finally,weappliedthismodel to predict metabolic pathways in UniProtKB/TrEMBL bacterial data which are poorly covered,currently3.5%.Acomparisonbetweentheannotationcoverageofthesystembeforeandafterself-trainingisshowninFigure1.3.RESULTSANDDISCUSSIONResults indicate thatwhile the systemperformswell in its original form (Precision = 98.4%, Recall=71.2%,AUC=86.2%), itsperformanceis improvedwithself-training,as illustratedbynoticeable increases inrecallandAUC(Precision=99.7%,Recall=89.4%,AUC=95.8%).

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Figure1providesstatistics fromtheUniProtKB/TrEMBLproteinsannotatedbyARBA.Withoutself-training,ARBAcovered4,564,250entries,where3,083,501proteins(denotedbyNew)lackedanypreviouspathwayannotations, resulting in an increase in pathway coverage from 3.5% to 7.4%, and 1,480,749 proteins(denotedbyOverlap)hadbeenpreviouslyannotatedbyUniProtpipelines.1,224,264proteins (denotedbyMissing) thathadbeenpreviouslyannotatedwerenotcoveredbyARBA. Incomparison,withself-training,thenumberofproteinsexclusivelyannotatedbyARBAwasincreasedto6,687,267,resultinginacoverageof12.1%. The number of missed entries was notably reduced to 394,205. These results demonstrate thebenefits of using self-training algorithms to provide functional annotations for UniProtKBwheremanuallycuratedannotationsarerare.4.AVAILABILITYModels for pathway prediction, generatedwith andwithout self-training based on release 2017_02 alongwith a Java Archive (JAR) package for applying them to UniProtKB bacterial data are available athttp://www.ebi.ac.uk/~rsaidi/arba/self-training/.5.REFERENCES1. The UniProt Consortium. 2017. UniProt: the universal protein knowledgebase. Nucleic Acids Research45(D1):D158-D169.2.ZhuX.2005.Semi-supervisedlearningliteraturesurvey(ReportNo.1530).ComputerSciences,UniversityofWisconsin-Madison.3.BoudelliouaI.etal.2016.PredictionofMetabolicPathwayInvolvementinProkaryoticUniProtKBDatabyAssociationRuleMining.PLOSONE11(7):e0158896.4.PedruzziI.etal.2015.HAMAPin2015:updatestotheproteinfamilyclassificationandannotationsystem.NucleicAcidsResearch43(D1):D1064-D1070.

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SessionVIII:Interactomics&miRNA

LeveragingTaxonomicRanksforImprovingMirrorTree-basedProteinInteractionPrediction

ErdemTurk*andBarisSuzek

MuglaSitkiKocmanUniversity{erdemturk@mu.edu.tr}

Proteins as one of themainstays of living organisms. Proteins’ functional subunits, namely domains,physically interactwitheachotherand this is critical toprotein’s functional and/or structural roles inbiological systems. Identification of domain-domain interactions (DDIs) and, consequently, proteininteractionsisimportantinunderstandingofrespectiveprotein’sfunctionalroles.

Using computational methods to predict DDIs is a fast and inexpensive way to complementexperimental studies. These methods typically identify potential interacting proteins for furtherexperimentalvalidation.Inliterature,thereareseveralmethodsforDDIpredictionincludingDPEA(Rileyetal.,2005),PE(Guimarãesetal.,2006),DIPD(Zhaoetal.,2009),MLE(Dengetal.,2002),DIMA(Pagelet al., 2004), RDFF (Chen and Liu, 2005) and RCDP (Jothi et al., 2006). RDCP is a MirrorTree-basedmethod used to predict protein-protein interactions based on coevolution with the premise thatinteractingproteinsevolvetogetherandtheytendtohavesimilarphylogenetictrees.Thus,similarityofphylogenetic trees, computed as correlation coefficient between similarity matrices for respectivedomains,isusedtoanswerwhethertwodomains(andproteins)areinteractingwitheachotherornot.Unfortunately MirrorTree is known to be sensitive to taxonomic diversity of domains (Zhou andJakobsson,2013).

Inthiswork,weproposeanewDDIpredictionmethod.OurmethodisbasedonMirrorTree’sbutcopeswithtaxonomicdiversityproblembyintroducinganewtaxonomy-rankbasedstepinsamplingdomainsthatwillbeusedincomputationofsimilaritymatrices.Insteadofusingdomainsrepresentingspecies(orlower)leveltaxonomicranks,weselectahigherleveltaxonomicrank(e.g.family)andrandomlysampledomains representing each possible taxa at this level. The similarity matrices are computed for thesampledsetsandguaranteedtomaintainacertainleveloftaxonomicdiversity.

Wehavetestedourmethodwithdifferenttaxonomicranksrangingfromspeciestoclassanddifferentsample sizes (10 to 100 proteins). For comparison with other methods, we used the 6,634experimentally verified DDIs from DOMINE (Yellaboina et al., 2011) database as a positive set andgeneratedanequalsizenegativesetbyrandomlypairingdomainswithnoknowninteractions.Witha71.0%sensitivityand63.0%specificityourmethodoutperformsalltheexistingDDIpredictionmethodswith the exception of RDFF, which has rather limited coverage within DOMINE gold set (148 DDIpredictions). Theagreementbetweenourmethodand theotherpredictionmethods rangesbetween(67and75%)basedonDOMINE’sgoldset.

In conclusion, our method’s performance positions it as a viable alternative to existing predictionmethods. Furthermore, given the overlap between predictionsmade by ourmethod and others, ourmethodcanbeusedtocomplementothermethodsinameta-DDIpredictor.

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UseofmiRNAinstrugglingofVarroaparasite

SedefErkunt*,NecatiAltindis,AhmetEfeKoseogluandCemalUn

EgeUniversity{erkuntsedef@gmail.com}

Beekeepinghasan important role inworldeconomybycontributing toagriculturalproductionandhumanfood production. In terms of number of bee colonies, Turkey ranks second in the world with 4.5 millioncoloniesafterChina,andabout50thousandfamiliesareearnedalivinginthiswork.Therearemanyfactorsthataffect this importanteconomicactivitynegatively.Thebiggestproblem inbeekeeping for thepast50yearsisVarroa(Varroadestructor)parasite.

InvestigationsinNewZealandhavereportedthatapproximately$400-900millionbudgetover35yearswerespentagainstforVarroaandonly55-70milliondollarsinNewZealandwereusedforeradicationofparasites.AlsoinTurkey,thereferencesfromBeekeepersAssociationindicatethatatotalof22.5millionTurkishLirasperyearwerespentfor4.5millionhivesinthefightagainstVarroaparasite.

Varroa,whilesuckingthebloodoftheadultbeesdirectly,causestheyoungindividualswhowillbeleavingtobeweakandwinglessandalsocausestheextinctionofthehiveinextremecases.ManymethodsofstruggleagainstVarroahavebeenused,withtheresultthatpesticideshavegainedmostweight.However,mostofthemethodshavenotbeensuccessfulenoughandpesticideshaveharmedbothhumanbeingsandhumanhealthbyleavingresidueonthehoney.

TheaimofthestudyistoproduceanewbiologicalproductforstruggleofVarroaparasite.Forthispurpose,Varroagenomewas targeted forending theviabilityofparasite.5differentgene regions (ATPsynthase8,cytochromeb,cytochromeoxidaseI,sodiumchannelprotein,NADHdehydrogenasesubunit2)wereselectedfromVarroa genome andmiRNA sequences targeting these regionswere designed by using bioinformatictools. It has been confirmed that the designed miRNA sequences do not target the bee and the humangenome. The synthesizedmiRNA sequences were applied to hives by spraying and the expression of thetargetedgeneregionswasmeasuredinVarroasamplescollected.

Afterthefirstapplicationinthestudy,2generegionswiththehighestdecreaseinexpressionamountweredeterminedandthesegeneregionswereusedinthesecondapplication.AsaresultofsecondapplicationtheexpressionanalysisshowedthatthemiRNAsequencewhichtargetsNADHdehydrogenasesubunit2hasthehighestinfluenceonVarroaparasite.

37

POSTERSESSION

Poster# Authors Title

1 AbdulahadBayraktar AStudyofCommonPathwaysofNeurodegenerativeDiseases

2 AbubakhariSserwaddaandÖmerSinanSaraç

Investigatingtheeffectofdifferentfeatureselectionstrategiesforclassificationofgeneexpressionsignaturesoftumorcells

3AhmetEfeKoseoglu,MortezaHaghi,IsmailKarabozandCemalUn

DetectionofHeatShockProtein(DnaK,DnaJandGrpE)HorizontalGeneTransfersAmongAcanthamoebapolyphaga,AcanthamoebaPolyphagaMimivirus(APMV),Amoeba-InfectingBacteriaandSputnikVirophage

4AhmetEfeKoseoglu,MuhammetUslupehlivan,ZarifaOsmanliandCemalUn

PosttranslationalModificationsofα-ConotoxinandTheirEffectonFeedingTypeofConusSpecies

5AhmetFarukAcar,JohanSanmartinBerglundandPeterAnderberg

SpokenDialogueInterfacefortheSmart4mdApplication

6*

AhmetSüreyyaRifaioğlu,MariaMartin,RengulCetin-Atalay,VolkanAtalayandTuncaDoğan

InvestigationofMulti-taskDeepNeuralNetworksinAutomatedProteinFunctionPrediction

7

AlperenDalkiran,AhmetSüreyyaRifaioğlu,TuncaDogan,VolkanAtalay,MariaMartinandRengulCetin-Atalay

PredictionofEnzymaticPropertiesofProteinSequencesBasedontheECNomenclature

8* ArifYılmazandYeşimAydınSon LosslessPruningofAHPSNPPrioritizatonTreeUsingRandomForestVariableImportances

9 AydanurŞentürk,ErdemSanalandNurhanÖzlü GlobalPhosphoproteomeAnalysisofXenopus

laeviseggextracts

38

10AyhanSerkanŞik,YeşimAydınSon,ErginSoysalandArsevUmurAydınoğlu

AConceptualDesignforGeneticInformationExchangeCodingStandardsinTurkey

11AyşeDeryaCavga,MehmetTardu,AttilaGürsoy,ÖzlemKeskinandHalilKavaklı

Cryptochromemutationsinp53mutantmiceincreaseanti-carcinogenicpathwaysanddamageresponses

12AyşegülTombuloğlu,HülyaÇöpoğlu,TülinGürayandYeşimAydınSon

ChangesinGeneExpressionProfileofHumanHepatocellularCarcinomaCellLine(Hepg2)InducedbyBoricAcidatHalfMaximalInhibitoryConcentration(IC50)

13 BesteMimaroğluAltınayandYeşimAydınSon

ADataMiningApproachforDirectingthePatienttoAppropriateTreatmentMethodinPainClinic

14BunyaminKasap,SerbulentUnsal,ZelihaKasap,AybarCanAcarandKemalTurhan

AMethodProposalforRevealingEpigeneticImmune-EscapeMechanismsUsing450kMethylationArrayData.

15 BurçinKurt,SerbülentÜnsal,İlknurBuçanKırkbirandKemalTurhan

SurvivalandEnrichmentAnalyzesofCisplatin-BasedDrugResistance-RelatedGenesforNon-SmallCellLungCancer

16

BurcuKarakaya,GizemKars,NeginRazizadeh,ÇağlaEceOlgun,PelinYaşar,GamzeAyazandMesutMuyan

Assessingputativeproteinpartnersofselectedestrogenresponsivegeneproductsbyayeast-two-hybridapproach#

17 BurcuTuranlı-YıldızandMeteYılmaz MolecularPhylogeneticsofHemeOxygenase

inCyanobacteria

18 BurcuYaldizandYesimAydinSon StructuralPropertiesofMethylatedHumanPromoterRegionsinTermsofDNAHelicalRise

19

ÇağlaEceOlgun,GamzeAyaz,BurcuKarakaya,GizemKars,NeginRazizadeh,PelinYaşar,NurcanTuncbağandMesutMuyan

TowardsproteomicanalysisofYPEL2interactingpartnersidentifiedwithproximity-dependentbiotinylation#

39

20 CanFirtina,A.ErcumentCicekandCanAlkan AProfileHMM-basedhybriderrorcorrection

algorithmforlongsequencingreads

21 CansuDincerandNurcanTuncbag StructuralModelingofthePatient-SpecificSignalingNetworksinGlioblastoma

22* CesimErten,EvisHoxha,HilalKazanandEsraTepe Identificationofdysregulatedpathwaysacross

multiplecancertypes

23DamlaGozen,HumaShehwana,OzlenKonu,TuncaDoganandRengulAtalay

Theidentificationofoxidativestressrelatedgeneprofilesinlivercancercellsastargetsfordiagnosticsandtherapeutics

24*ElifBozlak,YetkinAlıcı,EvrimFer,MelikeDönertaş,RasmusNielsenandMehmetSomel

Neighbouringsequencecontext-basedfixationbiasofmutationsinchimpanzeegenome

25* EmrahAkkoyun,AybarCanAcar,ByronZambranoandSeungikBaek CardiovascularModellingforAbdominalAortic

Aneurysms

26 ErdemŞanalandNurhanOzlu QuantitativeandNetworkAnalysisofCytokinesisSpecificPhosphoproteins

27* ErdemTurkandBarisSuzek LeveragingTaxonomicRanksforImprovingMirrorTree-basedProteinInteractionPrediction

28*EzgiKaraca,JoãoRodrigues,AndreaGraziadei,AlexandreBonvinandTeresaCarlomagno

AnIntegrativeFrameworkforStructureDeterminationofMolecularMachines

29 FaridehHalakou,EmelSenKilic,OzlemKeskinandAttilaGursoy MultipleConformationsofProteinsEnhance

Protein-proteinInteractionNetworks

30* FatihKaraoglanoglu,MarziehEslamiRasekhandCanAlkan DiscoveringLargeGenomicInversionsUsing

Long-rangeInformation

31

GamzeAyaz,PelinYaşar,BurcuKarakaya,ÇağlaEceOlgun,GizemKars,NeginRazizadeh,ŞeymaÜnsal,NurcanTunçbağandMesutMuyan

Proteininteractionapproachestoassignafunctionforanestrogenresponsivegeneprotein:CXXC5#

40

32 GökçeSengerandNurcanTunçbağ NetworkModelingoftheDasatinibTreatmentinGlioblastomaStemCellsbyDataIntegration

33

GökhanÖzsarı,AhmetSureyyaRifaioglu,TuncaDoğan,RengülÇetin-AtalayandMehmetVolkanAtalay

HierarchicalSubcellularLocalizationPredictionusingSupportVectorMachines

34 GuldenOlgun,OzgurSahinandOznurTastan DiscoveringBreastCancerSubtypeSpecific

lncRNAMediatedceRNAInteractions

35 GülşahKaradumanBahçeandYeşimAydınSon Genome-wideAnalysisofSpliceAcceptorSite

Motifs

36 GungorBudak,ErnestFraenkelandNurcanTuncbag

IdentificationofthePathwayLevelIschemicChangesbyIntegratingTemporalPhosphoproteomeinOvarianCancer

37* GuvanchOvezmyradov

Integrativebioinformaticsanalysisofmulti-omicsdatafacilitatesfunctionalcharacterizationofthecandiatetumorsuppressorproteinCTCF

38 HabibeCansuDemirelandNurcanTuncbag TheEffectofAlternativeSplicingonTumor

SpecificProteinNetworks

39* HandanMelikeDönertaşandJanetThornton DrugRepurposingforAgeing:AConnectivity

MapApproach

40 HaticeBüşraKonuk,MuhammetRaşitCesurandAlperYılmaz

IdentificationofTissueSpecificGenesandAssessmentofTheirIntersectionwithDifferentiallyExpressedGenesinCancerDataToUnderstandTumorHeterogeneityInSilico

41 HuseyinAlperDömandYesimAydınSon AFeatureSelectionModelforGenomeWide

AssociationStudiesofSchizophrenia

42IdilYet,PooryaParvizi,UlasIsildak,ZelihaGozdeTuranandMehmetSomel

StudyingstructuralvariationamongneuronsatoldageandinAlzheimer'sDiseaseusingsinglecellwholegenomesequencing

41

43* İlknurBuçanKırkbir,BurçinKurtandKemalTurhan AComputerAidedDiagnosisSystemforHeart

AttackUsingDecisionTree

44*

KorayAçıcı,ÇağatayBerkeErdaş,TunçAşuroğlu,MünireKılınçToprak,HamitErdemandHasanOğul

WearableSolutionsforParkinson'sDiseaseMonitoring

45KubraNarci,TuncaDogan,AybarCanAcar,TulinErsahin,NurcanTuncbagandRengulCetinAtalay

SystemsBiologyAnalysisofKinaseInhibitorsinCancerCellsUsingNextGenerationSequencingData

46 MalikYousef,WaleedKhalifaandLoaiAbdallah EnsembleClusteringClassificationAppliedon

PlantmicroRNAsData

47 MehmetOzcan,TurgutBastug,AliOsmanAcarandYaseminAksoy

ComputationalassessmentofthemostpotentialinhibitorsforGlutathioneS-transferaseP1-1

48 MeltemEdaOmur,EsrefCelik,BilalKermanandEmreKarakoc

MyelinInteractome:IdentificationofCell-CellInteractionsinMultipleSclerosisViaBipartiteGraphBasedProtein-ProteinInteractionNetworks

49 MericKinali,TuncaDoganandRengulCetin-Atalay

DifferentialanalysisofHepatocellularCarcinomaKinomebyChemicalandGeneticKnockouts

50 MerihAlphanKaradeniz,ErkanUnalMumcuogluandGuyPerkins

AutomaticSegmentationofCristaein3DElectronMicroscopyTomographyImagesUsinganArtificialNeuralNetworkandDirectionalGrowing

51MerveAkkulak,EmreEvin,ÖzlemDurukan,GüneşÖzhanandOrhanAdalı

EffectofresveratrolonmRNAandproteinexpressionsofVitaminDmetabolizingCYP24A1inhumanembryonickidneycellline(HEK-293)

52* MonaShojaei,EceAkhan,AybarCanAcarandRengülÇetinAtalay

IdentificationofGeneMutationsInvolvedinDrugResistanceinLiverCancerUsingRna-SeqDataAnalysis

42

53 MuhammetUslupehlivan,EcemŞenerandRemziyeDeveci InsilicoAnalysisofthePax6Protein

Glycosylation

54 MuhammetUslupehlivan,RemziyeDeveciandCemalÜn

InsilicoApproachtoRelationofPrionProteinGlycosylationwithScrapieDiseaseResistanceinDomesticSheep

55 MustafaTarımandCelalÖztürk MetabolicProfilingUsingPartialLeastSquaresDiscriminantAnalysiswithArtificialBeeColonyAlgorithm

56 NogayhanSeymenandEgeUlgen ComparisonofDifferentApproachestoClassifyRecurrenceofBreastCancer

57 NurcanTuncbagandErnestFraenkel Patient-specificNetworkModelingin

Glioblastoma

58*OguzhanBegik,MerveOyken,TunaCinkilliAlican,TolgaCanandAyseElifErsonBensan

Alternativepolyadenylationpatternsforcancerclassification

59 OğuzhanKalyonandAlperYılmaz SurveyofChimericmRNAsinHIVInfectedPatientRNA-SEQData

60ÖzlemDurukan,MerveAkkulak,SenaGjotaErgin,ŞevkiArslanandOrhanAdalı

Metformin-CisplatincombinationtreatmentaltersgeneandproteinexpressionsofCYP17A1inLNCaPcellline

61

ÖzlemÖzkan,YeşimAydınSon,ArsevUmurAydınoğlu,AhmetYalçın,AlperDöm,BurcuYaldız,KübraNarcıandOnurBaloğlu

ParticipatoryDesignMeetings:GeneticInformationIncludedPersonalHealthRecordApplication

62 PooryaParvizi,MehmetSomelandNurcanTuncbag NetworkAnalysisofTranscriptomicChanges

DuringAging

63* RabieSaidi,MaryamAbdollahyanandMariaMartin ASelf-trainingApproachforFunctional

AnnotationofUniProtKBProteins

64* RemziÇelebi,ErkanYaşar,ÖzgürGümüşandOğuzDikenelli RDFGraphEmbeddingsforPredictionofDrug-

DrugInteractions

43

65RumeysaFayetörbay,TulinErsahin,RengülCetinAtalayandNurcanTunçbağ

Network-BasedDiscoveryofDrugTreatmentsinHepatocellularCarcinoma

66 SaberHafezqoraniandHilalKazan Genome-wideanalysisforidentificationoflncRNAsthatspongeRNA-bindingproteins

67* SedefErkunt,NecatiAltindis,AhmetEfeKoseogluandCemalUn UseofmiRNAinstrugglingofVarroaparasite

68*SerenaMuratcıoğlu,HyunbumJang,AttilaGursoy,OzlemKeskinandRuthNussinov

Interactionoffarnesylated,butlikelydepalmitoylated,RasisoformswithPDEδ

69* SılaÖzdemir,AttilaGursoyandOzlemKeskin

Analysisofsingleaminoacidvariationsinhotspotandhotregionresiduesofprotein-proteininteractioninterfaces

70TubaEge,AyşeMineGenÇler-Özkan,AlaattinŞenandOrhanAdalı

FolkMedicinalPlantEpilobiumhirsutumL.andItsIngredientEllagicAcidAltersProteinandmRNAExpressionsofRatLiverBileAcidSynthesizingCYP7A1

71* TundeAderinwaleandHilalKazan IntegratingMultipleDataTypesforCancerSubtypeDiscovery

72 UğurSezerman,ZeynepÖzkeserliandBegümÖzemek

PossibleUsageofExpressionofCertainCLOCKGenesasapredictorofsurvivalinAMLcancerpatients

73 UğurToprak,SerbülentÜnsalandKemalTurhan

ComparingSurvivalTimesofLungSquamousCellCarcinomaPatientsfromCombinedRNA-seqandMethylationDatabyEstimatingRiskGroups

74UmutAgyuz,AhmetMelihOten,OktayKaplanandSuleymanNezihHekim

DoesMitochondrialGenesPasstoFetusFromMotherorFather

75 YusufBayrakceken,ZelihaGunnurDikmenandFilizAkbiyik TheCalculationofReferenceChangeValuefor

CoagulationTestParameters

44

76 YvesYannickYameniNoupoue BiometricEncryption:FromfingerprintImagetopersonaluniqueandirrevocablekey.

77 Hamitİzgi,MehmetSomel Meta-AnalysisofAlzheimer'sDiseaseattheGeneExpressionLevel

78

CanKoşukcu,AslıKüçükosmanoğlu,YaseminAlanay,PınarKavak,NilüferBerker,EkimZ.Taşkıran,MehmetAlikaşifoğlu,UğurSezerman,NurtenA.Akarsu

IdentificationofMolecularPathologyofPeters’AnomalySegregatinginaLargeAutosomalDominantFamily

*Thesesubmissionsarealsoacceptedasoralpresentation

45

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