Leying Guan
Associate Professor of BiostatisticsDownloadHi-Res Photo
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Associate Professor of 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.
Last Updated on April 07, 2025.
Appointments
Biostatistics
Associate Professor on TermPrimary
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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 Research Interests
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, MSc
Albert C Shaw, MD, PhD
Akiko Iwasaki, PhD
Leqi Xu
39Publications
566Citations
Computational Biology
Machine Learning
Publications
2026
MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics
Xu L, Dong Y, Zeng X, Bian Z, Zhou G, Guan L, Zhao H. MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics. Nature Genetics 2026, 58: 1583-1594. PMID: 42265311, PMCID: PMC13364660, DOI: 10.1038/s41588-026-02637-4.Peer-Reviewed Original ResearchAltmetricEmpiric azithromycin alters the upper respiratory microbiome and resistome without anti-inflammatory benefit in COVID-19
Glascock A, Maguire C, Phan H, Lydon E, Schaenman J, Calfee C, Melamed E, Greenland J, Corry D, Kheradmand F, Baden L, Sekaly R, McComsey G, Haddad E, Cairns C, Geng L, Pulendran B, Fernandez-Sesma A, Simon V, Metcalf J, Agudelo Higuita N, Messer W, Davis M, Nadeau K, Kraft M, Bime C, Erle D, Atkinson M, Brakenridge S, Ehrlich L, Montgomery R, Shaw A, Hough C, Hafler D, Augustine A, Becker P, Peters B, Ozonoff A, Hoch A, Kim-Schulze S, Krammer F, Bosinger S, Eckalbar W, Altman M, Wilson M, Guan L, Maecker H, Steen H, Diray-Arce J, Rouphael N, Kleinstein S, Jayavelu N, Reed E, Levy O, Chu V, Langelier C. Empiric azithromycin alters the upper respiratory microbiome and resistome without anti-inflammatory benefit in COVID-19. Nature Microbiology 2026, 11: 1100-1112. PMID: 41840216, PMCID: PMC13056551, DOI: 10.1038/s41564-026-02285-8.Peer-Reviewed Original ResearchCitationsAltmetricA benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios
Shen X, He C, Guan L. A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios. PLOS Computational Biology 2026, 22: e1014008. PMID: 41838767, PMCID: PMC13020996, DOI: 10.1371/journal.pcbi.1014008.Peer-Reviewed Original ResearchCitationsAuthor Correction: Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors
Doni Jayavelu N, Samaha H, Wimalasena S, Hoch A, Gygi J, Gabernet G, Ozonoff A, Liu S, Milliren C, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Schaenman J, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Agudelo Higuit N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Geng L, Fernandez Sesma A, Simon V, Krammer F, Kraft M, Bime C, Calfee C, Erle D, Langelier C, Guan L, Maecker H, Peters B, Kleinstein S, Reed E, Augustine A, Diray-Arce J, Becker P, Rouphael N, Altman M. Author Correction: Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors. Communications Medicine 2026, 6: 125. PMID: 41730996, PMCID: PMC12929789, DOI: 10.1038/s43856-026-01425-9.Commentaries, Editorials and LettersMachine learning models predict long COVID outcomes based on baseline clinical and immunologic factors
Doni Jayavelu N, Samaha H, Wimalasena S, Hoch A, Gygi J, Gabernet G, Ozonoff A, Liu S, Milliren C, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Schaenman J, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Agudelo Higuit N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Geng L, Fernandez Sesma A, Simon V, Krammer F, Kraft M, Bime C, Calfee C, Erle D, Langelier C, Guan L, Maecker H, Peters B, Kleinstein S, Reed E, Augustine A, Diray-Arce J, Becker P, Rouphael N, Altman M. Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors. Communications Medicine 2026, 6: 1. PMID: 41484172, PMCID: PMC12764860, DOI: 10.1038/s43856-025-01230-w.Peer-Reviewed Original ResearchCitationsAltmetric
2025
Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality
Narendra R, Lydon E, Van Phan H, Spottiswoode N, Neyton L, Diray-Arce J, Network I, Consortium C, Consortium E, Becker P, Kim-Schulze S, Hoch A, Pickering H, van Zalm P, Cairns C, Altman M, Augustine A, Bosinger S, Eckalbar W, Guan L, Jayavelu N, Kleinstein S, Krammer F, Maecker H, Ozonoff A, Peters B, Rouphael N, Montgomery R, Reed E, Schaenman J, Steen H, Levy O, Haller S, Erle D, Hendrickson C, Krummel M, Matthay M, Woodruff P, Haddad E, Calfee C, Langelier C. Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality. JCI Insight 2025, 10: e195436. PMID: 41212055, PMCID: PMC12643502, DOI: 10.1172/jci.insight.195436.Peer-Reviewed Original ResearchCitationsAltmetricA multi-omics recovery factor predicts long COVID in the IMPACC study
Gabernet G, Maciuch J, Gygi J, Moore J, Hoch A, Syphurs C, Chu T, Jayavelu N, Corry D, Kheradmand F, Baden L, Sekaly R, McComsey G, Haddad E, Cairns C, Rouphael N, Fernandez-Sesma A, Simon V, Metcalf J, Higuita N, Hough C, Messer W, Davis M, Nadeau K, Pulendran B, Kraft M, Bime C, Reed E, Schaenman J, Erle D, Calfee C, Atkinson M, Brakenridge S, Melamed E, Shaw A, Hafler D, Augustine A, Becker P, Ozonoff A, Bosinger S, Eckalbar W, Maecker H, Kim-Schulze S, Steen H, Krammer F, Westendorf K, Network I, Peters B, Fourati S, Altman M, Levy O, Smolen K, Montgomery R, Diray-Arce J, Kleinstein S, Guan L, Ehrlich L. A multi-omics recovery factor predicts long COVID in the IMPACC study. Journal Of Clinical Investigation 2025, 135: e193698. PMID: 40924481, PMCID: PMC12582403, DOI: 10.1172/jci193698.Peer-Reviewed Original ResearchCitationsAltmetricBaseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study
Hou J, Haslund-Gourley B, Diray-Arce J, Hoch A, Rouphael N, Becker P, Augustine A, Ozonoff A, Guan L, Kleinstein S, Peters B, Reed E, Altman M, Langelier C, Maecker H, Kim S, Montgomery R, Krammer F, Wilson M, Eckalbar W, Bosinger S, Levy O, Steen H, Rosen L, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Schaenman J, Shaw A, Hafler D, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Agudelo Higuita N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Fernandez Sesma A, Simon V, Kraft M, Bime C, Calfee C, Erle D, Impacc Network, Robinson L, Cairns C, Haddad E, Comunale M. Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study. Frontiers In Medicine 2025, 12: 1604388. PMID: 40687705, PMCID: PMC12271175, DOI: 10.3389/fmed.2025.1604388.Peer-Reviewed Original ResearchCitationsAltmetricType 2 immune responses are associated with less severe COVID-19 in a hospitalized cohort
Jayavelu N, Qi J, Milliren C, Ozonoff A, Liu S, Levy O, Baden L, Melamed E, McComsey G, Cairns C, Schaenman J, Shaw A, Hafler D, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Higuita N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Geng L, Sesma A, Simon V, Krammer F, Bime C, Calfee C, Bosinger S, Eckalbar W, Steen H, Maecker H, Becker P, Augustine A, Holland S, Rosen L, Lee S, Vaysman T, Ozonoff A, Diray-Arce J, Chen J, Kho A, Milliren C, Hoch A, Chang A, McEnaney K, Barton B, Lentucci C, Murphy M, Saluvan M, Shaheen T, Liu S, Syphurs C, Albert M, Hayati A, Bryant R, Abraham J, Thomas S, Cooney M, Karoly M, Altman M, Jayavelu N, Presnell S, Kohr B, Jancsyk T, Arnett A, Peters B, Overton J, Vita R, Westendorf K, Overton J, Levy O, Steen H, van Zalm P, Fatou B, Smolen K, Viode A, van Haren S, Jha M, Stevenson D, Odumade O, Baden L, Mendez K, Lasky-Su J, Tong A, Rooks R, Desjardins M, Sherman A, Walsh S, Mitre X, Cauley J, Li X, Evans B, Montesano C, Licona J, Krauss J, Issa N, Chang J, Izaguirre N, Hutton S, Michelotti G, Wong K, Tebbutt S, Shannon C, Sekaly R, Fourati S, McComsey G, Harris P, Sieg S, Ribeiro S, Cairns C, Haddad E, Kutzler M, Bernui M, Cusimano G, Connors J, Woloszczuk K, Joyner D, Edwards C, Lee E, Lin E, Melnyk N, Powell D, Kim J, Goonewardene I, Simmons B, Smith C, Martens M, Croen B, Semenza N, Bell M, Furukawa S, McLin R, Tegos G, Rogowski B, Mege N, Ulring K, Schearer P, Sheidy J, Nagle C, Seyfert-Margolis V, Rouphael N, Bosinger S, Boddapati A, Tharp G, Pellegrini K, Johnson B, Panganiban B, Huerta C, Anderson E, Samaha H, Sevransky J, Bristow L, Beagle E, Cowan D, Hamilton S, Hodder T, Bechnak A, Cheng A, Mehta A, Ciric C, Spainhour C, Carter E, Scherer E, Usher J, Hellmeister K, Hussaini L, Hewitt L, Mcnair N, Ribeiro S, Wimalasena S, Fernandez-Sesma A, Simon V, Krammer F, Van Bakel H, Kim-Schulze S, Reiche A, Qi J, Lee B, Carreño J, Singh G, Raskin A, Tcheou J, Khalil Z, van de Guchte A, Farrugia K, Khan Z, Kelly G, Srivastava K, Eaker L, Bermúdez-González M, Mulder L, Beach K, Saksena M, Altman D, Kojic E, Sominsky L, Azad A, Bielak D, Kawabata H, Yellin T, Fried M, Sullivan L, Morris S, Kleiner G, Stadlbauer D, Dutta J, Xie H, Patel M, Nie K, Rahman A, Messer W, Hough C, Siegel S, Sullivan P, Lu Z, Brunton A, Strand M, Lyski Z, Coulter F, Micheleti C, Maecker H, Pulendran B, Nadeau K, Rosenberg-Hasson Y, Leipold M, Sigal N, Rogers A, Fernandes A, Manohar M, Do E, Chang I, Lee A, Blish C, Din H, Roque J, Geng L, Artandi M, Davis M, Ahuja N, Yang S, Chinthrajah S, Hagan T, Reed E, Schaenman J, Salehi-Rad R, Rivera A, Pickering H, Sen S, Elashoff D, Ward D, Brook J, Sanchez E, Llamas M, Perdomo C, Magyar C, Fulcher J, Erle D, Calfee C, Hendrickson C, Kangelaris K, Nguyen V, Lee D, Chak S, Ghale R, Gonzalez A, Jauregui A, Leroux C, Altamirano L, Rashid A, Willmore A, Woodruff P, Krummel M, Carrillo S, Ward A, Langelier C, Patel R, Wilson M, Dandekar R, Alvarenga B, Rajan J, Eckalbar W, Schroeder A, Fragiadakis G, Tsitsiklis A, Mick E, Guerrero Y, Love C, Maliskova L, Adkisson M, Leligdowicz A, Beagle A, Rao A, Sigman A, Samad B, Curiel C, Shaw C, Tietje-Ulrich G, Milush J, Singer J, Vasquez J, Tang K, Betancourt L, Santhosh L, Pierce L, Paz M, Matthay M, Thakur N, Rodriguez N, Sutter N, Jones N, Sinha P, Prasad P, Lota R, Rashid S, Asthana S, Bhide S, Lea T, Abe-Jones Y, Hafler D, Montgomery R, Shaw A, Kleinstein S, Gygi J, Pawar S, Konstorum A, Chen E, Cotsapas C, Wang X, Xu L, Dela Cruz C, Iwasaki A, Mohanty S, Nelson A, Zhao Y, Farhadian S, Asashima H, Chaudhary O, Coppi A, Fournier J, Muenker M, Nelson A, Raddassi K, Rainone M, Ruff W, Salahuddin S, Shulz W, Vijayakumar P, Wang H, Wunder E, Young H, Ko A, Wang X, Duchen D, Esserman D, Guan L, Brito A, Rothman J, Grubaugh N, Corry D, Kheradmand F, Song L, Nelson E, Metcalf J, Higuita N, Sinko L, Booth J, Drevets D, Brown B, Kraft M, Bime C, Mosier J, Erickson H, Schunk R, Kimura H, Conway M, Francisco D, Molzahn A, Wilson C, Schunk R, Hughes T, Sierra B, Atkinson M, Brakenridge S, Ungaro R, Manning B, Moldawer L, Oberhaus J, Guirgis F, Borresen B, Anderson M, Ehrlich L, Melamed E, Maguire C, Wylie D, Rousseau J, Hurley K, Geltman J, Siles N, Rogers J, Augustine A, Diray-Arce J, Haddad E, Sekaly R, Kraft M, Woodruff P, Erle D, Ehrlich L, Montgomery R, Becker P, Altman M, Fourati S. Type 2 immune responses are associated with less severe COVID-19 in a hospitalized cohort. Journal Of Allergy And Clinical Immunology Global 2025, 4: 100515. PMID: 40709330, PMCID: PMC12284355, DOI: 10.1016/j.jacig.2025.100515.Peer-Reviewed Original ResearchCitationsFTO inhibition enhances the therapeutic index of radiation therapy in head and neck cancer
Ji L, Pu L, Wang J, Cao H, Melemenidis S, Sinha S, Guan L, Laseinde E, von Eyben R, Richter S, Nam J, Kong C, Casey K, Graves E, Frock R, Le Q, Rankin E. FTO inhibition enhances the therapeutic index of radiation therapy in head and neck cancer. JCI Insight 2025, 10: e184968. PMID: 40485587, PMCID: PMC12220955, DOI: 10.1172/jci.insight.184968.Peer-Reviewed Original ResearchCitations
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News
- May 14, 2025
Faculty Research Awards Showcase YSPH Strengths in Science
- 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
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