2025
Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction
Wang M, Patsenker J, Li H, Kluger Y, Kleinstein S. Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. PLOS Computational Biology 2025, 21: e1012153. PMID: 40163503, PMCID: PMC12013870, DOI: 10.1371/journal.pcbi.1012153.Peer-Reviewed Original ResearchConceptsSupervised fine-tuningImmune responseMolecular basis of antigen recognitionSARS-CoV-2 spike proteinLanguage modelSARS-CoV-2 vaccinesAdaptive immune responsesSpecific predictionsMolecular basisSpike proteinAntibody-based therapeuticsFine-tuningAntibody-antigen specificitySpecific to antigensInfluenza hemagglutininVaccine designAntigen recognitionModel embeddingsImmune functionLanguage model embeddingsSARS-CoV-2AntibodiesAntigenInfluenzaVaccineSIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
Dong M, Su D, Kluger H, Fan R, Kluger Y. SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data. Nature Communications 2025, 16: 2990. PMID: 40148341, PMCID: PMC11950362, DOI: 10.1038/s41467-025-58089-7.Peer-Reviewed Original ResearchConceptsOmics dataSpatial omics dataAnalysis of gene expressionSingle-cell resolutionDownstream analysisCellular statesSpatial interaction modelsGerminal center B cellsGene expressionCommunication machineryOmics technologiesIntercellular interactionsSpatial omics technologiesTumor microenvironmentB cellsSpatial dynamicsHuman tonsilsMacrophage stateSpatial effects
2019
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nature Methods 2019, 16: 243-245. PMID: 30742040, PMCID: PMC6402590, DOI: 10.1038/s41592-018-0308-4.Peer-Reviewed Original Research
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
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