2020
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Vaid A, Somani S, Russak A, De Freitas J, Chaudhry F, Paranjpe I, Johnson K, Lee S, Miotto R, Richter F, Zhao S, Beckmann N, Naik N, Kia A, Timsina P, Lala A, Paranjpe M, Golden E, Danieletto M, Singh M, Meyer D, O'Reilly P, Huckins L, Kovatch P, Finkelstein J, Freeman R, Argulian E, Kasarskis A, Percha B, Aberg J, Bagiella E, Horowitz C, Murphy B, Nestler E, Schadt E, Cho J, Cordon-Cardo C, Fuster V, Charney D, Reich D, Bottinger E, Levin M, Narula J, Fayad Z, Just A, Charney A, Nadkarni G, Glicksberg B. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal Of Medical Internet Research 2020, 22: e24018. PMID: 33027032, PMCID: PMC7652593, DOI: 10.2196/24018.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAdolescentAdultAgedAged, 80 and overBetacoronavirusCohort StudiesCoronavirus InfectionsCOVID-19Electronic Health RecordsFemaleHospital MortalityHospitalizationHospitalsHumansMachine LearningMaleMiddle AgedNew York CityPandemicsPneumonia, ViralPrognosisRisk AssessmentROC CurveSARS-CoV-2Young AdultConceptsElectronic health recordsNew York CityYork CityMount Sinai Health SystemSinai Health SystemMortality predictionAdmitted to hospitalAt-risk patientsHealth recordsHealth systemEHR dataIn-hospital mortalityEarly identification of high-risk patientsCOVID-19Identification of high-risk patientsMultiple hospitalsStudy populationPatient characteristicsSingle hospitalHospitalArea under the receiver operating characteristic curveEarly identificationPredicting mortalityCohort of patientsCOVID-19 pandemicAnalysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts
Huckins L, Chatzinakos C, Breen M, Hartmann J, Klengel T, da Silva Almeida A, Dobbyn A, Girdhar K, Hoffman G, Klengel C, Logue M, Lori A, Maihofer A, Morrison F, Nguyen H, Park Y, Ruderfer D, Sloofman L, van Rooij S, Consortium P, Baker D, Chen C, Cox N, Duncan L, Geyer M, Glatt S, Im H, Risbrough V, Smoller J, Stein D, Yehuda R, Liberzon I, Koenen K, Jovanovic T, Kellis M, Miller M, Bacanu S, Nievergelt C, Buxbaum J, Sklar P, Ressler K, Stahl E, Daskalakis N. Analysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts. Cell Reports 2020, 31: 107716. PMID: 32492425, PMCID: PMC7359754, DOI: 10.1016/j.celrep.2020.107716.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCase-Control StudiesCohort StudiesDexamethasoneDown-RegulationGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseHumansLeukocytesMaleMiceMice, Inbred C57BLMilitary PersonnelPrefrontal CortexRepressor ProteinsRibonucleoproteins, Small NuclearRNA InterferenceRNA, Small InterferingStress Disorders, Post-TraumaticConceptsPost-traumatic stress disorderGenetically regulated gene expressionPost-traumatic stress disorder casesDorsolateral prefrontal cortexGene expressionU12 intron splicingPrefrontal cortexStress disorderDeployment stressTranscriptome imputationTissue-specific gene expressionDifferential gene expressionMilitary cohortZNF140U12 intronsGenetic heterogeneityExpression changesFunctional roleExogenous glucocorticoidsPeripheral leukocytesEuropean cohortCortexCohortDisordersExpression
2019
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik G, Graff M, Nishimura K, Tao R, Haessler J, Gignoux C, Highland H, Patel Y, Sorokin E, Avery C, Belbin G, Bien S, Cheng I, Cullina S, Hodonsky C, Hu Y, Huckins L, Jeff J, Justice A, Kocarnik J, Lim U, Lin B, Lu Y, Nelson S, Park S, Poisner H, Preuss M, Richard M, Schurmann C, Setiawan V, Sockell A, Vahi K, Verbanck M, Vishnu A, Walker R, Young K, Zubair N, Acuña-Alonso V, Ambite J, Barnes K, Boerwinkle E, Bottinger E, Bustamante C, Caberto C, Canizales-Quinteros S, Conomos M, Deelman E, Do R, Doheny K, Fernández-Rhodes L, Fornage M, Hailu B, Heiss G, Henn B, Hindorff L, Jackson R, Laurie C, Laurie C, Li Y, Lin D, Moreno-Estrada A, Nadkarni G, Norman P, Pooler L, Reiner A, Romm J, Sabatti C, Sandoval K, Sheng X, Stahl E, Stram D, Thornton T, Wassel C, Wilkens L, Winkler C, Yoneyama S, Buyske S, Haiman C, Kooperberg C, Le Marchand L, Loos R, Matise T, North K, Peters U, Kenny E, Carlson C. Genetic analyses of diverse populations improves discovery for complex traits. Nature 2019, 570: 514-518. PMID: 31217584, PMCID: PMC6785182, DOI: 10.1038/s41586-019-1310-4.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesComplex traitsBiology of complex traitsDiverse populationsEvidence of effect-size heterogeneityGenome-wide effortsLarge-scale genomic studiesReduce health disparitiesNon-European individualsHighest burden of diseaseMulti-ethnic participantsEffect-size heterogeneityBurden of diseaseRepresentation of diverse populationsGWAS associationsNovel lociRisk prediction scoreAdmixed populationsFine-mappingGenetic architectureAssociation studiesGenomic studiesHealth disparitiesHealthcare disparitiesPopulation Architecture