2020
How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles
Zuo X, Chen Y, Ohno-Machado L, Xu H. How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles. Briefings In Bioinformatics 2020, 22: 800-811. PMID: 33757278, PMCID: PMC7799277, DOI: 10.1093/bib/bbaa331.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCoronavirus: indexed data speed up solutions
Ohno-Machado L, Xu H. Coronavirus: indexed data speed up solutions. Nature 2020, 584: 192-192. PMID: 32782375, DOI: 10.1038/d41586-020-02331-3.Commentaries, Editorials and LettersiDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching
Kuo T, Jiang X, Tang H, Wang X, Bath T, Bu D, Wang L, Harmanci A, Zhang S, Zhi D, Sofia H, Ohno-Machado L. iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching. BMC Medical Genomics 2020, 13: 98. PMID: 32693816, PMCID: PMC7372776, DOI: 10.1186/s12920-020-0715-0.Peer-Reviewed Original ResearchPrivacy challenges and research opportunities for genomic data sharing
Bonomi L, Huang Y, Ohno-Machado L. Privacy challenges and research opportunities for genomic data sharing. Nature Genetics 2020, 52: 646-654. PMID: 32601475, PMCID: PMC7761157, DOI: 10.1038/s41588-020-0651-0.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsEXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning
Kuo T, Gabriel R, Cidambi K, Ohno-Machado L. EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning. Journal Of The American Medical Informatics Association 2020, 27: 747-756. PMID: 32364235, PMCID: PMC7309256, DOI: 10.1093/jamia/ocaa023.Peer-Reviewed Original ResearchConceptsBlockchain technologyCentral serverServer-based methodBenefits of blockchainData protection policiesCentralized serverArtificial intelligenceModel learningDecentralized approachSmall datasetsBlockchainServerComputation strategySingle pointGeneralizable modelCost of efficiencyGenomic datasetsDatasetDistributed modelTechnologyGenomic dataMultiple institutionsDiscrimination powerIntelligencePotential advantages/disadvantages
2019
Evaluating and sharing global genetic ancestry in biomedical datasets
Harismendy O, Kim J, Xu X, Ohno-Machado L. Evaluating and sharing global genetic ancestry in biomedical datasets. Journal Of The American Medical Informatics Association 2019, 26: 457-461. PMID: 30869786, PMCID: PMC6433181, DOI: 10.1093/jamia/ocy194.Peer-Reviewed Original ResearchConceptsGenetic diversity measurementsGenetic ancestryAvailable molecular datasetsHuman genetics researchCancer Genome Atlas (TCGA) datasetContinental resolutionGenetic diversityPhenotype-genotype associationsMolecular datasetsGlobal genetic ancestryAncestry informationGenetic researchAtlas datasetDiversity measurementsAncestryTraitsGlobal scaleDiversityBiomedical datasetsAvailable datasetsData repositoryDisease riskAccess datasetDatasetAvailable cohorts
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 medicine
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 projectFeasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model.
Kim H, Choi J, Jang I, Quach J, Ohno-Machado L. Feasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model. AMIA Annual Symposium Proceedings 2017, 2016: 715-723. PMID: 28269868, PMCID: PMC5333244.Peer-Reviewed Original ResearchConceptsOMOP Common Data ModelNursing intervention protocolPatient reported outcomesCommon data modelReported outcomesDrug treatmentIntervention protocolUnique data itemsNursing articlesData modelNursing researchHealth dataPotential information lossData discoveryUse casesData itemsRepresenting DataFollowInformation lossDisease
2015
WebDISCO: a web service for distributed cox model learning without patient-level data sharing
Lu C, Wang S, Ji Z, Wu Y, Xiong L, Jiang X, Ohno-Machado L. WebDISCO: a web service for distributed cox model learning without patient-level data sharing. Journal Of The American Medical Informatics Association 2015, 22: 1212-1219. PMID: 26159465, PMCID: PMC5009917, DOI: 10.1093/jamia/ocv083.Peer-Reviewed Original Research
2014
Choosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery
Zhao Y, Wang X, Jiang X, Ohno-Machado L, Tang H. Choosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery. Journal Of The American Medical Informatics Association 2014, 22: 100-108. PMID: 25352565, PMCID: PMC4433380, DOI: 10.1136/amiajnl-2014-003043.Peer-Reviewed Original ResearchMeSH KeywordsConfidentialityDatasets as TopicDNAGenetic MarkersGenome, HumanGenome-Wide Association StudyHumansPolymorphism, Single NucleotideConceptsData ownersData usersHuman genomic datasetsHuman genomic dataPatient privacyPrivacyGeneration approachUsersData selectionReal dataDatasetGenomic datasetsPrivate solicitationDNA datasetsScientific discoveryNew approachGenomic dataHigh confidencePilot versionEvaluation methodRight choiceOwnersAlgorithmNew techniqueDisease marker discoveryBig Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates D, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 2014, 33: 1123-1131. PMID: 25006137, DOI: 10.1377/hlthaff.2014.0041.Commentaries, Editorials and LettersMeSH KeywordsData MiningDatasets as TopicDelivery of Health CareDisease ManagementElectronic Health RecordsHumansRisk FactorsTriageUnited StatesConceptsBig dataClinical analyticsPrivacy concernsUse casesElectronic health recordsAnalyticsTypes of dataHealth recordsTypes of insightsNecessary analysisSupport of researchHigh-cost patientsUnprecedented opportunityMonitoring devicesCostHealth careAlgorithmMultiple organ systemsRapid progressInfrastructureUS health care systemHealth care systemSystemAdverse eventsClinical data