Leying Guan
Assistant Professor of Biostatistics (Biostatistics)DownloadHi-Res Photo
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Assistant Professor of Biostatistics (Biostatistics)
Biography
Leying Guan is an Assistant Professor of Biostatistics at Yale University. She received her Ph.D from the Statistics department at Stanford in 2019. Her research primarily focuses on high dimensional statsitics, robust statistical learning, statistical inference and developing statistical and machine learning methods driven by scientific applications including genetics, immunology, and computational neuroscience.
Appointments
Biostatistics
Assistant ProfessorPrimary
Other Departments & Organizations
Research
Overview
High-dimensional Statistics; Statistical Inference; Outlier Detection; Machine Learning and Data Science; Statistical Genetics; Computational Neuroscience; Statistical Analysis of Immune Signatures in Human infection.
Medical Subject Headings (MeSH)
Computational Biology; Epigenomics; Gene Regulatory Networks; Genetics; Immune System Diseases; Machine Learning; Neurosciences; Statistics
ORCID
0000-0003-0609-1073
Research at a Glance
Yale Co-Authors
Frequent collaborators of Leying Guan's published research.
Publications Timeline
A big-picture view of Leying Guan's research output by year.
Research Interests
Research topics Leying Guan is interested in exploring.
Steven Kleinstein, PhD
Ruth R Montgomery, PhD
David A. Hafler, MD, FANA
Akiko Iwasaki, PhD
Albert Ko, MD
Bornali Bhattacharjee
24Publications
545Citations
Computational Biology
Machine Learning
Publications
2024
Gold-siRNA supraclusters enhance the anti-tumor immune response of stereotactic ablative radiotherapy at primary and metastatic tumors
Jiang Y, Cao H, Deng H, Guan L, Langthasa J, Colburg D, Melemenidis S, Cotton R, Aleman J, Wang X, Graves E, Kalbasi A, Pu K, Rao J, Le Q. Gold-siRNA supraclusters enhance the anti-tumor immune response of stereotactic ablative radiotherapy at primary and metastatic tumors. Nature Biotechnology 2024, 1-14. PMID: 39448881, DOI: 10.1038/s41587-024-02448-0.Peer-Reviewed Original ResearchAltmetricConceptsStereotactic ablative radiotherapyAnti-tumor immune responseSmall interfering RNAAblative radiotherapyMetastatic tumorsEffect of stereotactic ablative radiotherapyModel of head and neck cancerHead and neck cancerImmunosuppressive cell populationsPD-1 inhibitorsSmall interfering RNA complexesPD-1Primary tumorImmunotherapeutic effectsNeck cancerGranzyme BGal-1Mouse modelPassive deliveryTumorReduced toxicityCell populationsRadiotherapyRadiosensitivityRenal filtration thresholdA Survival Analysis of Patients with Radiation-Induced Cancers after Prior Radiation for Head and Neck Cancer
Laseinde E, Guan L, Hildebrand R, Meurice N, Gensheimer M, Beadle B, Holsinger F, Sunwoo J, Baik F, Sirjani D, Divi V, Kaplan M, Pinto H, Colevas A, Rahman M, Le Q. A Survival Analysis of Patients with Radiation-Induced Cancers after Prior Radiation for Head and Neck Cancer. International Journal Of Radiation Oncology • Biology • Physics 2024, 120: e764. DOI: 10.1016/j.ijrobp.2024.07.1678.Peer-Reviewed Original ResearchConceptsHead and neck cancerNonsurgical groupNeck cancerRadiation treatmentTreated with non-surgical therapyIRB-approved retrospective reviewSurvival rateKaplan-Meier survival curvesSurvival rate of patientsNon-surgical therapySurvival analysis of patientsLog-rank testAnalysis of patientsNon-surgical groupNon-surgical treatmentRate of patientsRadiation-induced cancerCompare survival ratesPrior RTPostoperative RTMedian followPrior radiationSquamous histologyOverall survivalMedian ageTert-expressing cells contribute to salivary gland homeostasis and tissue regeneration after radiation therapy
Guan L, Viswanathan V, Jiang Y, Vijayakumar S, Cao H, Zhao J, Colburg D, Neuhöfer P, Zhang Y, Wang J, Xu Y, Laseinde E, Hildebrand R, Rahman M, Frock R, Kong C, Beachy P, Artandi S, Le Q. Tert-expressing cells contribute to salivary gland homeostasis and tissue regeneration after radiation therapy. Genes & Development 2024, 38: 569-582. PMID: 38997156, PMCID: PMC11293384, DOI: 10.1101/gad.351577.124.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsSubmandibular glandSalivary gland homeostasisProgenitor cellsGland homeostasisResponse to radiotherapyAdult submandibular glandCell survivalSalivary gland regenerationSelf-renewal capacityEnhanced proliferative activityRadiation therapyDuctal regionsRadiotherapyModulate cell survivalTelomerase-expressingGland regenerationProliferative activityMouse strainsTERT expressionCreERT2 recombinaseSalivary gland biologyRadiation exposureTERT locusIn vitro cultureCell populationsIntegrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality
Gygi J, Maguire C, Patel R, Shinde P, Konstorum A, Shannon C, Xu L, Hoch A, Jayavelu N, Haddad E, Network I, Reed E, Kraft M, McComsey G, Metcalf J, Ozonoff A, Esserman D, Cairns C, Rouphael N, Bosinger S, Kim-Schulze S, Krammer F, Rosen L, van Bakel H, Wilson M, Eckalbar W, Maecker H, Langelier C, Steen H, Altman M, Montgomery R, Levy O, Melamed E, Pulendran B, Diray-Arce J, Smolen K, Fragiadakis G, Becker P, Sekaly R, Ehrlich L, Fourati S, Peters B, Kleinstein S, Guan L. Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality. Journal Of Clinical Investigation 2024, 134: e176640. PMID: 38690733, PMCID: PMC11060740, DOI: 10.1172/jci176640.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsClinical outcomesImmune cascadeElevated levels of inflammatory cytokinesDisease severityLevels of inflammatory cytokinesFormation of neutrophil extracellular trapsAcute COVID-19 severityCritically ill patientsNeutrophil extracellular trapsDevelopment of therapiesCOVID-19 cohortCOVID-19 severityViral clearanceImmunosuppressive metabolitesDeep immunophenotypingMultiomic modelIFN-stimulated genesImmunophenotypic assessmentB cellsDisease courseEarly upregulationInflammatory cytokinesDisease progressionIFN inhibitorsExtracellular trapsA 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMulti-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 reductionAbstract 5442: Terthighcells: key players in salivary gland homeostasis and regeneration after radiation therapy in adult mice
Guan L, Viswanathan V, V S, Cao H, Jiang Y, Zhao J, Colburg D, Neuhoefer P, Xu Y, Laseinde E, Artandi S, Le Q. Abstract 5442: Terthighcells: key players in salivary gland homeostasis and regeneration after radiation therapy in adult mice. Cancer Research 2024, 84: 5442-5442. DOI: 10.1158/1538-7445.am2024-5442.Peer-Reviewed Original ResearchConceptsSalivary gland homeostasisSubmandibular glandRadiation therapyGland homeostasisDuctal regionsAdult miceAmerican Association for Cancer Research annual meetingsProgenitor cellsAcinar cellsRadiation cell killingAdult submandibular glandCell survivalSelf-renewal capacityPost-radiotherapyPost-radiationModulate cell survivalStem/progenitor cellsNormal organsMouse strainsTERT expressionCell killingDuctal cellsOxidative stress response pathwayCreERT2 recombinaseTERT locusA multi-omics systems vaccinology resource to develop and test computational models of immunity
Shinde P, Soldevila F, Reyna J, Aoki M, Rasmussen M, Willemsen L, Kojima M, Ha B, Greenbaum J, Overton J, Guzman-Orozco H, Nili S, Orfield S, Gygi J, da Silva Antunes R, Sette A, Grant B, Olsen L, Konstorum A, Guan L, Ay F, Kleinstein S, Peters B. A multi-omics systems vaccinology resource to develop and test computational models of immunity. Cell Reports Methods 2024, 4: 100731. PMID: 38490204, PMCID: PMC10985234, DOI: 10.1016/j.crmeth.2024.100731.Peer-Reviewed Original ResearchCitationsAltmetric
2023
Smooth and Probabilistic PARAFAC Model with Auxiliary Covariates
Guan L. Smooth and Probabilistic PARAFAC Model with Auxiliary Covariates. Journal Of Computational And Graphical Statistics 2023, 33: 538-550. DOI: 10.1080/10618600.2023.2257783.Peer-Reviewed Original ResearchAltmetricEarly cellular and molecular signatures correlate with severity of West Nile virus infection
Lee H, Zhao Y, Fleming I, Mehta S, Wang X, Vander Wyk B, Ronca S, Kang H, Chou C, Fatou B, Smolen K, Levy O, Clish C, Xavier R, Steen H, Hafler D, Love J, Shalek A, Guan L, Murray K, Kleinstein S, Montgomery R. Early cellular and molecular signatures correlate with severity of West Nile virus infection. IScience 2023, 26: 108387. PMID: 38047068, PMCID: PMC10692672, DOI: 10.1016/j.isci.2023.108387.Peer-Reviewed Original ResearchCitationsAltmetricConceptsWest Nile virusEffective anti-viral responseInnate immune cell typesWest Nile virus infectionPro-inflammatory markersAcute time pointsImmune cell typesAnti-viral responseMolecular signaturesHost cellular activitiesAcute infectionAsymptomatic donorsPeripheral bloodSevere infectionsVirus infectionImmune responseSevere casesCell activityIll individualsSerum proteomicsInfectionInfection severityHigh expressionTime pointsNile virusDistinguishing features of long COVID identified through immune profiling
Klein J, Wood J, Jaycox J, Dhodapkar R, Lu P, Gehlhausen J, Tabachnikova A, Greene K, Tabacof L, Malik A, Silva Monteiro V, Silva J, Kamath K, Zhang M, Dhal A, Ott I, Valle G, Peña-Hernández M, Mao T, Bhattacharjee B, Takahashi T, Lucas C, Song E, McCarthy D, Breyman E, Tosto-Mancuso J, Dai Y, Perotti E, Akduman K, Tzeng T, Xu L, Geraghty A, Monje M, Yildirim I, Shon J, Medzhitov R, Lutchmansingh D, Possick J, Kaminski N, Omer S, Krumholz H, Guan L, Dela Cruz C, van Dijk D, Ring A, Putrino D, Iwasaki A. Distinguishing features of long COVID identified through immune profiling. Nature 2023, 623: 139-148. PMID: 37748514, PMCID: PMC10620090, DOI: 10.1038/s41586-023-06651-y.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsLong COVIDSARS-CoV-2Infection syndromeExaggerated humoral responseSoluble immune mediatorsEpstein-Barr virusPost-exertional malaiseCross-sectional studyHigher antibody responseImmune mediatorsImmune phenotypingImmune profilingHumoral responseAntibody responseLymphocyte populationsCOVID statusUnbiased machineCortisol levelsLC statusRelevant biomarkersViral pathogensSyndromeCOVIDFuture studiesBiological features
News & Links
News
- May 01, 2024
COVID-19: New ‘Omics’ Models Show Why Some People Are at Greater Risk of Severe Disease, Death
- June 09, 2023
Why Does COVID-19 Cause Severe Illness in Some Patients but Not Others?
- June 16, 2022
Understanding Poor Vaccine Responses in Individuals With Weakened Immune Systems
- August 11, 2021
Comprehensive COVID-19 Study Counts Yale Among Its Leaders
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