2023
Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program
Wang X, Ryu J, Kim J, Ramirez A, Mayo K, Condon H, Vaitinadin N, Ohno-Machado L, Talavera G, Ellinor P, Lubitz S, Choi S. Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program. Journal Of Human Genetics 2023, 68: 565-570. PMID: 37072623, PMCID: PMC10524735, DOI: 10.1038/s10038-023-01147-z.Peer-Reviewed Original ResearchConceptsDiverse human populationsGenomic dataGene-based burden testsWhole genome sequencesRare variant analysisHuman populationQuantitative traitsBurden testsRare lossLociComplex diseasesGenetic associationVariant analysisFunction variantsCardiometabolic traitsRare variantsTraitsBiomedical researchGIGYF1VariantsNPR2ACANSequencePopulationLDLR
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
2017
Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop
Jagodnik K, Koplev S, Jenkins S, Ohno-Machado L, Paten B, Schurer S, Dumontier M, Verborgh R, Bui A, Ping P, McKenna N, Madduri R, Pillai A, Ma'ayan A. Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop. Journal Of Biomedical Informatics 2017, 71: 49-57. PMID: 28501646, PMCID: PMC5545976, DOI: 10.1016/j.jbi.2017.05.006.Peer-Reviewed Original ResearchConceptsBig dataDigital objectsBig data scienceNIH Big DataDiversity of dataComputational infrastructureData scienceData sharingVirtual environmentSecure processK frameworkDiverse datasetsKnowledge initiativesKnowledge commonsSuch dataBiomedical researchObjectsInteroperabilityFrameworkDiscoverabilityRecent yearsSharingDatasetInfrastructurePilot project
2016
Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States
Wang S, Jiang X, Singh S, Marmor R, Bonomi L, Fox D, Dow M, Ohno‐Machado L. Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Annals Of The New York Academy Of Sciences 2016, 1387: 73-83. PMID: 27681358, PMCID: PMC5266631, DOI: 10.1111/nyas.13259.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsData privacySensitive individual informationComputer science communityReal-world problemsUnauthorized partiesHuman genomic dataPrivacy breachesData accessData sharingData accessibilityConfidentiality protectionGenomic dataSpectrum of techniquesIndividual informationPrivacyScience communityPhenotype informationTechnical approachPotential solutionsCurrent common practiceBiomedical researchResearch purposesConfidentialityInformationSharingSecure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)
Shi H, Jiang C, Dai W, Jiang X, Tang Y, Ohno-Machado L, Wang S. Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE). BMC Medical Informatics And Decision Making 2016, 16: 89. PMID: 27454168, PMCID: PMC4959358, DOI: 10.1186/s12911-016-0316-1.Peer-Reviewed Original ResearchConceptsData sharingPatient privacySecure multi-party computationModel learning phaseMulti-party computationBiomedical data sharingInformation leakageModel learningIntermediary informationInformation exchangeSecondary usePrivacyBig concernPractical solutionLogistic regression frameworkExperimental resultsSharingRegression frameworkFrameworkMultiple institutionsPrevious workComputationLearningBiomedical researchInformation
2013
Biomedical CyberInfrastructure challenges
Farcas C, Balac N, Ohno-Machado L. Biomedical CyberInfrastructure challenges. 2013, 1-4. DOI: 10.1145/2484762.2484767.Peer-Reviewed Original Research