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Jian Zhou

TitleAssistant Professor
InstitutionUniversity of Chicago
DepartmentMedicine-Genetic Medicine
AddressChicago IL 60637
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    Collapse Overview 
    Collapse overview
    We are interested in understanding how genomic sequences function through computational approaches. My lab currently focuses on developing machine learning and AI methods for decoding the logic of gene regulation, improving the interpretation of genetic variation, and enabling flexible design of regulatory sequences. Our broader interests span genomics, human genetics, evolution, machine learning, and AI.

    Collapse Biography 
    Collapse education and training
    Peking UniversityB.S.06/2011Biological Sciences
    Princeton UniversityPh.D.07/2017Quantitative and Computational Biology
    Collapse awards and honors
    2021 - 2026NIH Director's New Innovator Award

    Collapse Bibliographic 
    Collapse selected publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
    Newest   |   Oldest   |   Most Cited   |   Most Discussed   |   Timeline   |   Field Summary   |   Plain Text
    PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
    1. Zhang Y, Zhou J. Inverse Flow and Consistency Models . ICML 2025 (International Conference on Machine Learning). 2025. View Publication.
    2. Nmezi B, Rodriguez Bey G, Oranburg TD, Dudnyk K, Lardo SM, Herdman N, Jacko A, Rubio S, Loeza-Alcocer E, Kofler J, Kim D, Rankin J, Kivuva E, Gutowski N, Schon K, van den Ameele J, Chinnery PF, Sousa SB, Palavra F, Toro C, Pinto E Vairo F, Saute J, Pan L, Alturkustani M, Hammond R, Gros-Louis F, Gold MS, Park Y, Bernard G, Raininko R, Zhou J, Hainer SJ, Padiath QS. An oligodendrocyte silencer element underlies the pathogenic impact of lamin B1 structural variants. Nat Commun. 2025 Feb 05; 16(1):1373. PMID: 39910058; PMCID: PMC11799162.
      Citations: 1     Fields:    Translation:HumansAnimalsCells
    3. Dudnyk K, Cai D, Shi C, Xu J, Zhou J. Sequence basis of transcription initiation in the human genome. Science. 2024 04 26; 384(6694):eadj0116. PMID: 38662817; PMCID: PMC11223672.
      Citations: 39     Fields:    Translation:HumansAnimalsCells
    4. Botten GA, Zhang Y, Dudnyk K, Kim YJ, Liu X, Sanders JT, Imanci A, Droin N, Cao H, Kaphle P, Dickerson KE, Kumar KR, Chen M, Chen W, Solary E, Ly P, Zhou J, Xu J. Structural variation cooperates with permissive chromatin to control enhancer hijacking-mediated oncogenic transcription. Blood. 2023 07 27; 142(4):336-351. PMID: 36947815; PMCID: PMC10447518.
      Citations: 16     Fields:    Translation:HumansCells
    5. Avdeyev P, Shi C, Tan Y, Dudnyk K, Zhou J. Dirichlet Diffusion Score Model for biological sequence generation. ICML 2023 (International Conference on Machine Learning). 2023. View Publication.
    6. Chen KM, Wong AK, Troyanskaya OG, Zhou J. A sequence-based global map of regulatory activity for deciphering human genetics. Nat Genet. 2022 07; 54(7):940-949. PMID: 35817977; PMCID: PMC9279145.
      Citations: 125     Fields:    Translation:HumansCells
    7. Zhou J. Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale. Nat Genet. 2022 05; 54(5):725-734. PMID: 35551308; PMCID: PMC9186125.
      Citations: 93     Fields:    Translation:Cells
    8. Zhou J, Troyanskaya OG. An analytical framework for interpretable and generalizable single-cell data analysis. Nat Methods. 2021 11; 18(11):1317-1321. PMID: 34725480; PMCID: PMC8959118.
      Citations: 8     Fields:    Translation:HumansAnimals
    9. Zhou J, Park CY, Theesfeld CL, Wong AK, Yuan Y, Scheckel C, Fak JJ, Funk J, Yao K, Tajima Y, Packer A, Darnell RB, Troyanskaya OG. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019 06; 51(6):973-980. PMID: 31133750; PMCID: PMC6758908.
      Citations: 140     Fields:    Translation:HumansCells
    10. Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018 08; 50(8):1171-1179. PMID: 30013180; PMCID: PMC6094955.
      Citations: 286     Fields:    Translation:HumansCells
    11. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015 Oct; 12(10):931-4. PMID: 26301843; PMCID: PMC4768299.
      Citations: 990     Fields:    Translation:HumansCells
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