2021
Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study
, , Rouphael N, Maecker H, Montgomery R, Diray-Arce J, Kleinstein S, Altman M, Bosinger S, Eckalbar W, Guan L, Hough C, Krammer F, Langelier C, Levy O, McEnaney K, Peters B, Rahman A, Rajan J, Sigelman S, Steen H, van Bakel H, Ward A, Wilson M, Woodruff P, Zamecnik C, Augustine A, Ozonoff A, Reed E, Becker P, Higuita N, Altman M, Atkinson M, Baden L, Becker P, Bime C, Brakenridge S, Calfee C, Cairns C, Corry D, Davis M, Augustine A, Ehrlich L, Haddad E, Erle D, Fernandez-Sesma A, Hafler D, Hough C, Kheradmand F, Kleinstein S, Kraft M, Levy O, McComsey G, Melamed E, Messer W, Metcalf J, Montgomery R, Nadeau K, Ozonoff A, Peters B, Pulendran B, Reed E, Rouphael N, Sarwal M, Schaenman J, Sekaly R, Shaw A, Simon V. Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study. Science Immunology 2021, 6: eabf3733. PMID: 34376480, PMCID: PMC8713959, DOI: 10.1126/sciimmunol.abf3733.Peer-Reviewed Original ResearchConceptsCOVID-19 cohortProspective longitudinal studyHost immune responseLongitudinal studyCOVID-19Identification of biomarkersHospitalized patientsRespiratory secretionsClinical criteriaDisease progressionImmune responseRadiographic dataImmunologic assaysEffective therapeuticsOptimal timingStudy designBiologic samplingSuch interventionsCohortSeveritySample collectionAssay protocolsPatients
2017
Gating mass cytometry data by deep learning
Li H, Shaham U, Stanton KP, Yao Y, Montgomery RR, Kluger Y. Gating mass cytometry data by deep learning. Bioinformatics 2017, 33: 3423-3430. PMID: 29036374, PMCID: PMC5860171, DOI: 10.1093/bioinformatics/btx448.Peer-Reviewed Original ResearchRemoval of batch effects using distribution-matching residual networks
Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, Kluger Y. Removal of batch effects using distribution-matching residual networks. Bioinformatics 2017, 33: 2539-2546. PMID: 28419223, PMCID: PMC5870543, DOI: 10.1093/bioinformatics/btx196.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyCytophotometryData AccuracyHumansMachine LearningSequence Analysis, RNASingle-Cell AnalysisStatistics as TopicConceptsMeasurement errorNovel deep learning approachRandom measurement errorMultivariate distributionsResidual neural networkDeep learning approachNovel biological technologiesMaximum mean discrepancyPhysical phenomenaResidual networkNeural networkLearning approachSystematic componentSupplementary dataSystematic errorsMean discrepancyScRNA-seq datasetsBatch effectsErrorNetworkStatistical analysis
2008
RNA interference screen for human genes associated with West Nile virus infection
Krishnan MN, Ng A, Sukumaran B, Gilfoy FD, Uchil PD, Sultana H, Brass AL, Adametz R, Tsui M, Qian F, Montgomery RR, Lev S, Mason PW, Koski RA, Elledge SJ, Xavier RJ, Agaisse H, Fikrig E. RNA interference screen for human genes associated with West Nile virus infection. Nature 2008, 455: 242-245. PMID: 18690214, PMCID: PMC3136529, DOI: 10.1038/nature07207.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyDengue VirusEndoplasmic ReticulumGene Expression ProfilingGenome, HumanHeLa CellsHIVHumansImmunityMonocarboxylic Acid TransportersMuscle ProteinsProtein BindingRNA InterferenceUbiquitinationUbiquitin-Protein LigasesVesiculovirusVirus ReplicationWest Nile FeverWest Nile virus