2024
A supervised Bayesian factor model for the identification of multi-omics signatures
Gygi J, Konstorum A, Pawar S, Aron E, Kleinstein S, Guan L. A supervised Bayesian factor model for the identification of multi-omics signatures. Bioinformatics 2024, 40: btae202. PMID: 38603606, PMCID: PMC11078774, DOI: 10.1093/bioinformatics/btae202.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremComputational BiologyCOVID-19FemaleGenomicsHumansMultiomicsSupervised Machine LearningConceptsMulti-omics signaturesBayesian factor modelMulti-omics dataMulti-omics integrationSupplementary dataOmics datasetsMulti-omicsProfiling datasetsR packageDiverse assaysImproved biological understandingProfiling assaysSignature discoveryBioinformaticsProfiling studiesBiological understandingDimensionality reductionBiological responsesBiological signaturesCombination of dimensionality reduction
2019
Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data
Gadala-Maria D, Gidoni M, Marquez S, Heiden J, Kos JT, Watson CT, O'Connor KC, Yaari G, Kleinstein SH. Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data. Frontiers In Immunology 2019, 10: 129. PMID: 30814994, PMCID: PMC6381938, DOI: 10.3389/fimmu.2019.00129.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAllelesBayes TheoremGenotypeHumansImmunoglobulinsMyasthenia GravisSequence Analysis, DNA
2015
Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases
Gorenshteyn D, Zaslavsky E, Fribourg M, Park CY, Wong AK, Tadych A, Hartmann BM, Albrecht RA, García-Sastre A, Kleinstein SH, Troyanskaya OG, Sealfon SC. Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity 2015, 43: 605-614. PMID: 26362267, PMCID: PMC4753773, DOI: 10.1016/j.immuni.2015.08.014.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBayes TheoremComputational BiologyGene Regulatory NetworksHost-Pathogen InteractionsHumansImmune SystemImmune System DiseasesInternetProtein Interaction MappingProtein Interaction MapsReproducibility of ResultsSignal TransductionSupport Vector MachineTranscriptomeVirus DiseasesConceptsPublic high-throughput dataGenome-scale experimentsDisease-associated genesHigh-throughput datasetsHigh-throughput dataData-driven hypothesesGenetic studiesImmune pathwaysGenesImmunological diseasesFunctional relationshipBiomedical research effortsImportant interactionsMolecular entitiesImmune systemHuman immune systemProteinExponential growthPathwayData resourcesBayesian integrationRelevant insightsGrowthCompendiumIdentification
2012
Quantifying selection in high-throughput Immunoglobulin sequencing data sets
Yaari G, Uduman M, Kleinstein SH. Quantifying selection in high-throughput Immunoglobulin sequencing data sets. Nucleic Acids Research 2012, 40: e134-e134. PMID: 22641856, PMCID: PMC3458526, DOI: 10.1093/nar/gks457.Peer-Reviewed Original ResearchConceptsQuantifying selectionDifferent selection pressuresHigh-throughput immunoglobulinSomatic hypermutationNext-generation sequencing dataDNA mutation patternsSomatic mutation patternsGroups of sequencesAntigen-driven selection processMutation patternsSequence dataSelection pressureSequencing dataB cell affinity maturationB-cell cancersNegative selection