2023
A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia
Ohno-Machado L, Jiang X, Kuo T, Tao S, Chen L, Ram P, Zhang G, Xu H. A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia. Journal Of Biomedical Informatics 2023, 139: 104322. PMID: 36806328, PMCID: PMC10975485, DOI: 10.1016/j.jbi.2023.104322.Peer-Reviewed Original ResearchConceptsPrivacy of individualsAppropriate privacy protectionData-driven modelsPrivacy protectionPrivacy risksData Coordination CenterData hubData repositoryHierarchical strategyPrivacyBiomedical discoveryData setsRecord linkageData Coordinating CenterRepositoryComplex strategiesCoordination centerTechnologyTechniqueDataPartiesSetHierarchy
2022
The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition
Kuo T, Jiang X, Tang H, Wang X, Harmanci A, Kim M, Post K, Bu D, Bath T, Kim J, Liu W, Chen H, Ohno-Machado L. The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition. Journal Of The American Medical Informatics Association 2022, 29: 2182-2190. PMID: 36164820, PMCID: PMC9667175, DOI: 10.1093/jamia/ocac165.Peer-Reviewed Original ResearchConceptsSensitive personal informationGenomic data analysisPotential future research directionsPersonal informationSecurity concernsGenomics data repositoryData repositoryReport lessonsProtection techniquesFuture research directionsPrivacyResearch directionsData usePractical challengesGenomic dataData analysisAnonymizationCommunity effortsRepositorySecurityBiomedical researchInformationDataChallenges
2020
The Data Tags Suite (DATS) model for discovering data access and use requirements
Alter G, Gonzalez-Beltran A, Ohno-Machado L, Rocca-Serra P. The Data Tags Suite (DATS) model for discovering data access and use requirements. GigaScience 2020, 9: giz165. PMID: 32031623, PMCID: PMC7006671, DOI: 10.1093/gigascience/giz165.Peer-Reviewed Original ResearchConceptsData accessData discovery toolsPrivacy of subjectsData use agreementsConfidential dataMetadata itemsData reuseMetadata schemaAutomated systemDiscovery toolTechnical systemsStandard wayUse agreementsAccessPrivacyMetadataSchemaUse requirementsReuseResearchersResearch dataSystemRequirementsInformationData
2018
A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis.
Vaidya J, Shafiq B, Asani M, Adam N, Jiang X, Ohno-Machado L. A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis. AMIA Annual Symposium Proceedings 2018, 2017: 1695-1704. PMID: 29854240, PMCID: PMC5977652.Peer-Reviewed Original ResearchConceptsPrivacy-preserving approachData management systemBig dataBiomedical datasetsClassification taskBiomedical dataContext of regressionManagement systemSynthetic dataGeneration methodologyEssential problemResearch tasksAdditional datasetsDatasetTaskSignificant effortsDirect accessFirstorder approximationDataParticular typeAccessPrecision medicinePerfectly Secure and Efficient Two-Party Electronic-Health-Record Linkage
Chen F, Jiang X, Wang S, Schilling L, Meeker D, Ong T, Matheny M, Doctor J, Ohno-Machado L, Vaidya J. Perfectly Secure and Efficient Two-Party Electronic-Health-Record Linkage. IEEE Internet Computing 2018, 22: 32-41. PMID: 29867290, PMCID: PMC5983039, DOI: 10.1109/mic.2018.112102542.Peer-Reviewed Original ResearchPrivacy-preserving record linkagePrivacy/security concernsRecord linkage solutionsApproximate matching mechanismsPatient health dataSecurity concernsMatching mechanismLinkage solutionsHealth recordsHealth dataRecord linkageSecureSharingFirst stepPersonalized careEfficientPortable methodHealth record linkageAccessPrecision medicineData
2017
Finding useful data across multiple biomedical data repositories using DataMed
Ohno-Machado L, Sansone S, Alter G, Fore I, Grethe J, Xu H, Gonzalez-Beltran A, Rocca-Serra P, Gururaj A, Bell E, Soysal E, Zong N, Kim H. Finding useful data across multiple biomedical data repositories using DataMed. Nature Genetics 2017, 49: 816-819. PMID: 28546571, PMCID: PMC6460922, DOI: 10.1038/ng.3864.Peer-Reviewed Original ResearchConceptsBiomedical data repositoriesHealth big dataData setsKnowledge discoveryBig dataMultiple repositoriesSearch enginesData indexFAIR principlesDataMedData repositoryService providersKnowledge initiativesKnowledge expertsBiomedical research communityResearch communityRepositoryScience landscapeUseful dataInteroperabilityMetadataFindabilitySetEngineData
2016
PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS
Chen F, Wang S, Jiang X, Ding S, Lu Y, Kim J, Sahinalp S, Shimizu C, Burns J, Wright V, Png E, Hibberd M, Lloyd D, Yang H, Telenti A, Bloss C, Fox D, Lauter K, Ohno-Machado L. PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics 2016, 33: 871-878. PMID: 28065902, PMCID: PMC5860394, DOI: 10.1093/bioinformatics/btw758.Peer-Reviewed Original ResearchConceptsSoftware Guard ExtensionsHomomorphic encryptionDistributed ComputationCollaboration frameworkTrustworthy computationCollaboration modelSupplementary dataNetwork collaborationEncryptionExperimental resultsHealth informationAlternative solutionComputationInternational collaboration frameworkHardwareAccurate analysisPerformanceBioinformaticsImplementationFrameworkDataExtensionInformationCollaboration
2014
A community assessment of privacy preserving techniques for human genomes
Jiang X, Zhao Y, Wang X, Malin B, Wang S, Ohno-Machado L, Tang H. A community assessment of privacy preserving techniques for human genomes. BMC Medical Informatics And Decision Making 2014, 14: s1. PMID: 25521230, PMCID: PMC4290799, DOI: 10.1186/1472-6947-14-s1-s1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBiomedical dataPrivacy preserving techniquesPrivacy protection techniquesData privacyBiomedical computingHuman genomic dataData donorsDissemination techniquesPersonal Genome ProjectRaw dataProtection techniquesRigorous protectionPrivacyGenomic dataFinal resultsComputingCommunity effortsAnalysis outcomesChallengesTechniqueDataProject“Big Data” and the Electronic Health Record
Ross M, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook Of Medical Informatics 2014, 23: 97-104. PMID: 25123728, PMCID: PMC4287068, DOI: 10.15265/iy-2014-0003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBig dataEHR systemsElectronic health record systemsHealth record systemsData miningElectronic health recordsData applicationsActionable knowledgeMassive numberAdditional keywordsNew keywordsSecondary useInformatics professionalsHealth recordsRecord systemKeywordsLarge amountPrivacyNext stepMiningSecurityEHRSystemImplementationData
2013
Genomes in the cloud: balancing privacy rights and the public good.
Ohno-Machado L, Farcas C, Kim J, Wang S, Jiang X. Genomes in the cloud: balancing privacy rights and the public good. AMIA Joint Summits On Translational Science Proceedings 2013, 2013: 128. PMID: 24303320.Peer-Reviewed Original ResearchIdentifying inference attacks against healthcare data repositories.
Vaidya J, Shafiq B, Jiang X, Ohno-Machado L. Identifying inference attacks against healthcare data repositories. AMIA Joint Summits On Translational Science Proceedings 2013, 2013: 262-6. PMID: 24303279, PMCID: PMC3845790.Peer-Reviewed Original Research
2012
Preserving Institutional Privacy in Distributed binary Logistic Regression.
Wu Y, Jiang X, Ohno-Machado L. Preserving Institutional Privacy in Distributed binary Logistic Regression. AMIA Annual Symposium Proceedings 2012, 2012: 1450-8. PMID: 23304425, PMCID: PMC3540539.Peer-Reviewed Original ResearchGrid Binary LOgistic REgression (GLORE): building shared models without sharing data
Wu Y, Jiang X, Kim J, Ohno-Machado L. Grid Binary LOgistic REgression (GLORE): building shared models without sharing data. Journal Of The American Medical Informatics Association 2012, 19: 758-764. PMID: 22511014, PMCID: PMC3422844, DOI: 10.1136/amiajnl-2012-000862.Peer-Reviewed Original ResearchConceptsIntegrity of communicationCentralized data sourcesTraditional LR modelCentral repositoryComputational costData sourcesData setsSame formatPatient dataComputationGenomic dataRare patternRelevant dataLR modelPrediction valueSetRepositoryPartial elementsFormatClassificationCommunicationModelDataPatient setPerformiDASH: integrating data for analysis, anonymization, and sharing
Ohno-Machado L, Bafna V, Boxwala A, Chapman B, Chapman W, Chaudhuri K, Day M, Farcas C, Heintzman N, Jiang X, Kim H, Kim J, Matheny M, Resnic F, Vinterbo S, team A. iDASH: integrating data for analysis, anonymization, and sharing. Journal Of The American Medical Informatics Association 2012, 19: 196-201. PMID: 22081224, PMCID: PMC3277627, DOI: 10.1136/amiajnl-2011-000538.Commentaries, Editorials and LettersConceptsHigh-performance computing environmentPrivacy-preserving mannerCollaborative tool developmentData-sharing capabilitiesData ownersComputing environmentData consumersBiomedical computingHealth Insurance PortabilityTechnology researchTool developmentAccountability ActBiological projectsBiological dataInsurance PortabilityAnonymizationComputingPortabilityBehavioral researchersAlgorithmSoftwareCloudNew National CenterDataCapability
2004
A primer on gene expression and microarrays for machine learning researchers
Kuo W, Kim E, Trimarchi J, Jenssen T, Vinterbo S, Ohno-Machado L. A primer on gene expression and microarrays for machine learning researchers. Journal Of Biomedical Informatics 2004, 37: 293-303. PMID: 15465482, DOI: 10.1016/j.jbi.2004.07.002.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsNew algorithmSupervised learning modelUCI machineLearning modelMicroarray data analysisAlgorithmic developmentsTypes of dataMachineData setsMain challengesGene expression dataMain motivationAlgorithmData analysisBiomedical experimentsLarge numberExpression dataMicroarray dataResearchersRepositoryWebMicroarray experimentsNew waveDataSetThe Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer
Jaroszewicz S, Simovici D, Kuo W, Ohno-Machado L. The Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer. IEEE Transactions On Biomedical Engineering 2004, 51: 1095-1102. PMID: 15248526, DOI: 10.1109/tbme.2004.827267.Peer-Reviewed Original Research
1995
Hierarchical neural networks for survival analysis.
Ohno-Machado L, Walker M, Musen M. Hierarchical neural networks for survival analysis. Medinfo. 1995, 8 Pt 1: 828-32. PMID: 8591339.Peer-Reviewed Original ResearchConceptsNeural networkHierarchical neural networkHierarchical systemHierarchical modelHierarchical architectureDiscrete variablesNetworkData setsNonhierarchical modelTraditional methodsMedical applicationsAccurate predictionNumber of eventsArchitectureSystemTime-dependent variablesModelDataFirst time intervalTime intervalPredictionSetVariables