Christine Dien
she/her/hers
PhD Student, Computational Biology and Biomedical InformaticsAbout
Research
Publications
2025
Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study
Socrates V, Wright D, Huang T, Fereydooni S, Dien C, Chi L, Albano J, Patterson B, Kanaparthy N, Wright C, Loza A, Chartash D, Iscoe M, Taylor R. Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study. JMIR Aging 2025, 8: e69504. PMID: 40215480, PMCID: PMC12032504, DOI: 10.2196/69504.Peer-Reviewed Original ResearchKLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection
Fagerberg E, Attanasio J, Dien C, Singh J, Kessler E, Abdullah L, Shen J, Hunt B, Connolly K, De Brouwer E, He J, Iyer N, Buck J, Borr E, Damo M, Foster G, Giles J, Huang Y, Tsang J, Krishnaswamy S, Cui W, Joshi N. KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection. Science 2025, 387: eadn2337. PMID: 39946463, PMCID: PMC12199163, DOI: 10.1126/science.adn2337.Peer-Reviewed Original ResearchConceptsCD8 T cellsT cellsCD8 T cell exhaustionNaive CD8 T cellsAcute LCMV infectionT cell exhaustionT cell fate decisionsLineage fidelityLCMV infectionEffector differentiationAcute infectionExhaustion programTranscription factorsImmune responseEpigenetic modulationSuppress differentiationProgenitor stateKLF2InfectionFunctional stateFate decisionsCD8
2024
Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon
Otto D, Jordan C, Dury B, Dien C, Setty M. Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon. Nature Methods 2024, 21: 1185-1195. PMID: 38890426, PMCID: PMC12265947, DOI: 10.1038/s41592-024-02302-w.Peer-Reviewed Original Research
2023
SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad S, Choo Z, Dien C, Sohail N, Masilionis I, Chaligné R, Nawy T, Brown C, Sharma R, Pe’er I, Setty M, Pe’er D. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nature Biotechnology 2023, 41: 1746-1757. PMID: 36973557, PMCID: PMC10713451, DOI: 10.1038/s41587-023-01716-9.Peer-Reviewed Original ResearchConceptsCell statesTransposase-accessible chromatinSingle-cell sequencing dataSingle-cell dataDiscrete cell typesChromatin landscapeSequence dataGenomic dataExpression dynamicsAssociated with disease onsetCritical regulatorsGene scoreT cell differentiationCD4 T cell differentiationCell typesHematopoietic differentiationMetaCellChromatinCell clustersActive stateDifferentiationATACGenomeCellsRNA
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