2021
Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption
Froelicher D, Troncoso-Pastoriza J, Raisaro J, Cuendet M, Sousa J, Cho H, Berger B, Fellay J, Hubaux J. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nature Communications 2021, 12: 5910. PMID: 34635645, PMCID: PMC8505638, DOI: 10.1038/s41467-021-25972-y.Peer-Reviewed Original ResearchConceptsMultiparty homomorphic encryptionHomomorphic encryptionPrivacy-preserving analysisNecessary key stepMultiple healthcare institutionsFederated analyticsFederated settingAnalysis tasksAnalytics systemIntermediate dataEncryptionCentralized studiesPatient dataBiomedical insightsScientific collaborationAccurate resultsIndispensable complementAnalyticsHealthcare institutionsDatasetTaskSystemBiomedical researchAccessCollaborationPrivacy-preserving genotype imputation in a trusted execution environment
Dokmai N, Kockan C, Zhu K, Wang X, Sahinalp S, Cho H. Privacy-preserving genotype imputation in a trusted execution environment. Cell Systems 2021, 12: 983-993.e7. PMID: 34450045, PMCID: PMC8542641, DOI: 10.1016/j.cels.2021.08.001.Peer-Reviewed Original ResearchConceptsTrusted Execution EnvironmentExecution environmentHardware-based solutionsSide-channel attacksIntel SGXEnhanced securityPrivacy concernsAnalysis servicesImputation ServerServer limitData resourcesImputation algorithmSGXServerImputation softwareGenomic data resourcesImputation accuracyGenotype imputationImputation strategiesServicesDownstream analysisScalabilityImputationEssential toolSecurityAssessing single-cell transcriptomic variability through density-preserving data visualization
Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology 2021, 39: 765-774. PMID: 33462509, PMCID: PMC8195812, DOI: 10.1038/s41587-020-00801-7.Peer-Reviewed Original Research
2019
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
Hie B, Cho H, DeMeo B, Bryson B, Berger B. Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape. Cell Systems 2019, 8: 483-493.e7. PMID: 31176620, PMCID: PMC6597305, DOI: 10.1016/j.cels.2019.05.003.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic landscapeSingle-cell RNA sequencing studiesSingle-cell omicsCell typesSeq data integrationSingle-cell data analysisRare cell typesRNA sequencing studiesScRNA-seq dataTranscriptional diversityTranscriptomic landscapeBiological cell typesTranscriptomic heterogeneitySequencing studiesRare subpopulationAnalysis pipelineCellsUmbilical cord bloodEssential stepInflammatory macrophagesOmicsComprehensive visualizationDiversityGeometric sketchHundreds of thousands
2018
Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks
Cho H, Berger B, Peng J. Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks. Cell Systems 2018, 7: 185-191.e4. PMID: 29936184, PMCID: PMC6469860, DOI: 10.1016/j.cels.2018.05.017.Peer-Reviewed Original Research
2016
Reconstructing Causal Biological Networks through Active Learning
Cho H, Berger B, Peng J. Reconstructing Causal Biological Networks through Active Learning. PLOS ONE 2016, 11: e0150611. PMID: 26930205, PMCID: PMC4773135, DOI: 10.1371/journal.pone.0150611.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsBayes TheoremGene Regulatory NetworksHumansMachine LearningModels, BiologicalConceptsGaussian Bayesian networksBayesian networkContinuous Bayesian networksDiscrete Bayesian networksBiological networksFast convergenceGreat practical interestGene regulatory networksGraph structurePractical interestQuantitative propertiesSignificant runtime improvementsComplex biological systemsRuntime improvementCausal biological networksSystems biologyCentral problemFinal distributionPrevious approachesNetwork structureResource constraintsData setsLearning algorithmImportant modelActive learning algorithm
2015
Exploiting ontology graph for predicting sparsely annotated gene function
Wang S, Cho H, Zhai C, Berger B, Peng J. Exploiting ontology graph for predicting sparsely annotated gene function. Bioinformatics 2015, 31: i357-i364. PMID: 26072504, PMCID: PMC4542782, DOI: 10.1093/bioinformatics/btv260.Peer-Reviewed Original ResearchConceptsFunction prediction algorithmsPrediction algorithmVariety of algorithmsFunctional labelsOntology graphCross-validation experimentsOverfitting problemGraph structurePrevious stateGene functionAlgorithmAnnotation catalogsTens of thousandsMolecular interaction networksFunction predictionOntology databasePoor predictive performanceAnnotationLabelsPredictive performanceGene Ontology databaseInformationLarge numberGO termsInteraction networks