• Jingjing Li, Ph.D.

    Instructor, Department of Pediatrics

    Center for Genomics and Personalized Medicine

    Stanford University School of Medicine

    Address: 1265 Welch Rd., Stanford, CA 94305

    Email: lijj AT stanford.edu


    Research Summary

    My pre-doctoral training was in electrical engineering and machine learning. I did my Ph.D. in molecular genetics. As a Banting Postdoctoral Fellow with Dr. Michael Snyder at Stanford Genetics, I built integrative models together with multi-omics profiling technologies to derive deep clinical insights from disease genomes. At Stanford Pediatrics, I have also received training in clinical research with Drs. Gary Shaw and David K. Stevenson on identifying the molecular basis of many neonatal conditions.


    The main theme of my research is genome analysis for complex diseases by integrating multi-omics data, evolutionary analysis and electronic health records (EHR) into a predictive model powered by machine learning techniques. My study for the first time achieved accurate prediction of complex diseases solely from personal genomes (Li, et al., Cell, 2018), and was also the first to identify genes in bronchopulmonary dysplasia (BPD, a prevalent lung disease in pre-mature newborns) (Li, et al., American Journal of Respiratory and Critical Care Medicine (AJRCCM), 2015, the Blue Journal). My recent study integrated population genetic analysis, transcriptomic profiling and GWAS studies into a unified model, and illustrated the role of positive selection in shaping population-specific pregnancy-associated traits and identified introgressed Neanderthal alleles affecting human progesterone responsiveness (Li, et al., American Journal of Human Genetics (AJHG), 2018). Targeting autism spectrum disorders, my integrative analyses have uncovered novel genetic components in autism (Li, et al., Molecular Systems Biology, 2014, and Li, et al. Cell Systems, 2015). Taken together, my research is transdisciplinary by nature, bringing multi-omic and evolutionary insights and machine-learning techniques into clinical research to improve our understanding of complex human diseases.


    Ph.D. 2007-2011 in Computational Systems Biology

    Department of Molecular Genetics (Mentor: Dr.Zhaolei Zhang)

    Banting and Best Department of Medical Research

    Donnelly Centre for Cellular and Bio-molecular Research

    Faculty of Medicine, University of Toronto

    M.Sc. 2003-2006 in Machine Learning and Pattern Recognition

    Institute of Intelligent Machines,

    Graduate University of Chinese Academy of Sciences

    B.Sc. 1999-2003 in Electrical Engineering

    Department of Automatic Control

    School of Electrical Engineering and Information

    Sichuan University, China

    Professional Training

    2016 (September) – present

    Instructor,Division of Neonatal and Developmental Medicine

    Department of Pediatrics, Stanford University School of Medicine

    Mentors: David K. Stevenson, M.D., Gary Shaw, DrPh., and Michael Snyder, Ph.D.



    Banting Fellow,Department of Genetics, Stanford Center for Genomics and Personalized Medicine

    Mentor: Michael Snyder, Ph.D.

    Academic Services​

    I have served as a reviewer for many journals, including Genome Research, Nucleic Acid Research, eLife, PLOS Genetics, PLoS Computational Biology, Scientific Report, Molecular Biology & Evolution, Genome Biology & Evolution, and Bioinformatics, and also as a reviewer or a program committee member for international conferences on machine learning (e.g.IJCNN, ICIC).

    Certificates & Awards

    1. May 2019, the Annual Manuscript Award, the Cardiovascular Institute, Stanford University
    2. May 2014,  Banting Postdoctoral Fellowship, the Government of Canada (This is themost prestigious award for the very best postdoctoral applicants (both nationally and internationally, $70,000/year).
    3. Jul. 2014, Stanford Graduate School of Business (GSB) - IGNITE (The highly selected flagship program of Stanford GSB, which teaches core business skills, and helps develop business plans in corroboration with venture capitalists in Silicon Valley).
    4. Mar. 2011, Chinese Government Award for Outstanding Self-Financed Students Abroad (This is the most prestigious and competitive award administered by the Government of China to recognize the academic merits and research accomplishments of exceptional oversea Ph.D. students, $5,000 prize).
    5. Sep. 2010, L.W. Macpherson Microbiology Award, University of Toronto (This award is to recognize research excellence for graduate students in the Department of Molecular Genetics (one awardee per year, $1,000 prize)
    6. Sep. 2010, Open Fellowship, University of Toronto ($12, 000 prize for academic excellence).
    7. Jul. 2010, Jennifer Dorrington Award, University of Toronto (This award is to recognize research excellence for graduate students in the Banting and Best Department of Medical Research (shared with two other awardees in 2010, each $1,800 prize)
    8. Sep. 2008, Dr. Roman Pakula Award, University of Toronto (This award is to recognize research excellence for graduate students in the Department of Molecular Genetics (One awardee per year, $,1000 prize)
    9. Jul. 2008, Open Fellowship, University of Toronto ($12, 000 prize for academic excellence).
    10. Sep. 2006, Yongling Liu Scholarship (A national award to recognize the best-acclaimed graduate students in Chinese Academy of Sciences)
    11. Aug. 2005, Scholarship for Complex Systems Summer School, Santa Fe Institute, NM, USA (Full financial support from Santa Fe Institute for Complex Systems Summer School)
    12. Sep. 2004, the 2nd prize in the National Post-Graduate Mathematic Contest in Modeling (NPGMCM, 2004), China


    1. Jingjing Li, Minyi Shi, Michael Snyder, Methods of Diagnosing Autism Spectrum Disorders. 12/28/2017. US-2017-0369945-A1.
    2. Jingjing Li, Cuiping Pan, Sai Zhang, Philips Tsao, Michael Snyder, Processes of Genetic and Clinical Data Classification of Complex Human Traits.02/13/2018. 62/615,304 (Pending).
    3. Sai Zhang, Jingjing Li, Michael Snyder. Systems and Methods for Predicting Genetic Diseases.02/27/2018 (Pending).


    32. Li, J., Li, X., Zhang, S., Snyder, M. Gene-environment interaction in the era of precision medicine. Cell 2019, 177(1):38-44.


    31. Spiegel, A.M., Li, J., Oehlert, J.W., Mayo, J.A., Quaintance, C.C., Girsen, A.I., Druzin, M.L., El-Sayed, Y.Y., Shaw, G.M., Stevenson, D.K., Gibbs, R.S. A genome-wide analysis of clinical chorioamnionitis among preterm infants. American Journal of Perinatology 2019.


    30. Stevenson, D.K., Wong, R.J., Shaw, G.M., Li, J., Wise, P.H., and Davis, J.M. The contributions of genetics to premature birth. Pediatric Research 2019, 85:416-417.


    29. Stevenson, D.K., Wong, R.J., Aghaeepour, N., Angst, M.S., Darmstadt, G.L., DiGiulio, D.B., Druzin, M.L., Gaudilliere, B., Gibbs, R.S., Gould, J.B., Katz, M., Li, J., Moufarrej, M.N., Quaintance, C.C., Quake, S.R., Relman, D.A., Shaw, G.M., Snyder, M., Wang, X., Wise, P.H. Understanding health disparities. Journal of Perinatology 2019,39(3):354-358.


    28. Li, J.*, Pan, C.*, Zhang, S*, Spin, J.M., Deng, A., Dalman, R.L., Tsao, P.S., Snyder, M. Decoding the genomics of abdominal aortic aneurysm.Cell 2018, 174: 1361-1372 (Research Article).

    # Featured in Nature: How to warn of a pulsating artery that could burst any time

    # Press release from Stanford Medicine, Cell Press, Science Daily, etc.


    27. Li, J., Hong, X., Mesiano, S., Muglia, L.J., Wang, X. Snyder, M. Stevenson, D.K., Shaw, G.M. Natural selection has differentiated the progesterone receptor among human populations. American Journal of Human Genetics (AJHG) 2018,103:1-13.

    # Featured by Nature: Gene important in pregnancy shows evolution in action

    # Press Release by Stanford Medicine: Genetic variation in progesterone receptor tied to prematurity risk


    26. Li, J., Oehlert, J., Snyder M., Stevenson, D.K., and Shaw, G.M. Fetal de novo mutations and preterm birth. PLoS Genetics 2017 13(4): e1006689.


    25. Yu, K*, Li, J.*, Snyder M., Shaw, G.M., and O’Brodvich, H.M.The genetic predisposition to

    bronchopulmonary dysplasia. Current Opinion in Pediatrics 2016, 28(3):318-323 (*co-first author).


    24.Li, J., Ma, Z., Shi, M., Malty, R.H., Aoki, H.A., Minic, Z., Phanse, S., Jin, K., Wall, D.P., Zhang, Z., Urban, A.E., Hallmayer, J., Babu, M., and Snyder, M. Identification of human neuronal protein complexes reveals biochemical activities and convergent mechanisms of action in autism spectrum disorders. Cell Systems 2015, 1(5): 361-374.

    # Also see the preview article by Dr. Haiyuan Yu and colleagues: Study Autism in Context. Cell Systems, 2015, 1(5):312-313.


    23. Li, J.*, Yu, K.H.*, Oehlert, J., Jeliffe-Pawlowski, L.L., Gould, J.B., Stevenson, D.K., Snyder, M., Shaw, G.M., and O’Brodvich, H.M. Exome sequencing of neonatal blood spots identifies genes implicated in bronchopulmonary dysplasia. American Journal of Respiratory and Critical Care Medicine (AJRCCM) 2015, 192:589-596 (the Blue Journal).

    # Also see the editorial by Dr. Steven Abman et al.: Genomic Insights into Respiratory Outcomes after Preterm Birth, American Journal of Respiratory and Critical Care Medicine 2015, 192:530-532.


    22. Li, J.*, Shi, M.*, Ma, Z.*, Zhao, S., Euskirchen, G., Ziskin, J., Urban, A., Hallmayer, J., and Snyder, M. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Molecular Systems Biology 2014, 10:774.

    # Also see the News & Views by Dr. Charles Auffray: Autism cornered: network analyses reveal mechanisms of autism spectrum disorders. Molecular Systems Biology 2014, 10:778.

    # Highlighted by Nature Genetics, 2015, 47:105.

    # Media coverage by EMBO Press, Los Angeles Times, FOX News, Science Daily, GenomeWeb.


    21. Li, J.*, Kim, T.*, Nutiu, R., Ray, D., Hughes, T.R., and Zhang, Z. Identifying mRNA sequence elements for target recognition by human Argonaute proteins. Genome Research 2014, 24(5):775-785.

    # Interviewed by BioTechniQues: Argonaute Protein Preferences


    20. Cheng, Y., Ma, Z., Kim, B.H., Wu, W., Cayting, P., ..., Dogan, N., Li, J., Euskirchen, G., ..., Snyder, M. Principles of regulatory information conservation between mouse and human. Nature 2014, 515: 371–375.


    19. Kasowski, M.,...,Li, J., Xie, D., .., Snyder, M. Extensive variation in chromatin states across humans. Science 2013, 342(6159):750-752.


    18. Li, J. and Zhang, Z. microRNA regulatory variation in human evolution. Trends in Genetics 2013, 29(2): 116-124.


    17. Xin, X., Gfeller, D., ..., Li, J., Cheng, A.T., ..., Vidal, M., Boone, C., Sidhu, S.S., and Bader, G.D. SH3 interactome conserves general function over specific form. Molecular Systems Biology 2013, 9:652.


    16. Li, J., Liu, Y., Xin, X., Kim, T., Cabeza, E.A., Ren, J., Nielsen, R., Wrana, J. and Zhang, Z. Evidence for positive selection on a number of microRNA regulatory interactions during recent human evolution. PLoS Genetics 2012, 8(3): e1002578.


    15. Li, Z.*, Vizeacoumar, F.J.*, Bahr, S., Li, J., Warringer, J., Vizeacoumar, F.S., VanderSluis, B., Bellay, J., DeVit, M., Fleming, J.A., Stephens, A., Haase, J., Lin, Z.-Y., Baryshnikova, A., Min, R., Lu, H., Yan, Z., Jin, K., Datti, A., Nislow, C., Costanzo, M., Bulawa, C., Myers, C.L., Gingras, A., Zhang, Z., Blomberg, A., Bloom, K., Andrews, B., and Boone, C. Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nature Biotechnology 2011, 29: 361-367.


    14. Jin, K., Li, J., Vizeacoumar, F.S., Li, Z., Min, R. Zamparo, L., Vizeacoumar, F.J., Datti, A., Andrews, B., Boone, C., and Zhang, Z. PhenoM: a morphological database of essential genes in Saccharomyces cerevisiae. Nucleic Acids Research 2011, 40(D1): D687-D694.


    13. Li, J., Min, R., Vizeacoumar, F.J., Jin, K., Xin, X. and Zhang, Z. Exploiting the determinants of stochastic gene expression in S. cerevisiae for genome-wide prediction of expression noise. PNAS 2010, 107(23): 10472-10477.


    12. Li, J., Liu, Y., Kim, T., Min, R., and Zhang, Z. Gene expression variability within and between human populations and implications toward disease susceptibility. PLoS Computational Biology 2010, 6(8): e1000910.


    11. Li, J., Yuan, Z. and Zhang, Z. The cellular robustness by genetic redundancy in budding yeast. PLoS Genetics 2010, 6(11): e1001187.


    10. Li, J., Yuan, Z. and Zhang, Z. Revisiting the contribution of cis elements to expression divergence between duplicated genes: the role of chromatin structure. Molecular Biology and Evolution 2010, 27(7): 1461-1466.


    9. Li, J., Liu, Y., Dong, D. and Zhang, Z. Evolution of an X-linked primate-specific microRNA cluster. Molecular Biology and Evolution 2010, 27(3): 671-683.


    8. Vizeacoumar, F.J., van Dyk, N., Vizeacoumar, F.S., Cheung, V., Li, J., Sydorsky, Y., Case, N., Li, Z., Datti, A., Nislow, C., Raught, B., Zhang, Z., Frey, B., Bloom, K., Boone, C. and Andrews, B.J. Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis. Journal of Cell Biology 2010, 188: 69-81.


    7. Li J., Min R., Bonner A., and Zhang Z. A probabilistic framework to improve microRNA target prediction by incorporating proteomics data. Journal of Bioinformatics and Computational Biology 2009, 7(6): 955-972.


    6. Min, R., Bonner, A., Li, J.and Zhang, Z. Learned random-walk kernels and empirical-map kernels for protein sequence classification. Journal of Computational Biology 2009, 16(3): 457-474.


    5. Li, J., Musso, G., and Zhang, Z. Preferential regulation of duplicated genes by microRNAs in mammals. Genome Biology 2008, 9:R132.

    # Highlighted Research in Nature Review Genetics 2008, 9:734.


    4. Li, J., Huang,D.S., Wang, B. and Chen, P. Identifying protein-protein interfacial residues in heterocomplexes using residue conservation scores. International Journal of Biological Macromolecules 2006, 38(3-5): 241-247.


    3. Li, J., Huang, D.S., Lok, T.-M., Lyu, M.R., Li, Y.-X., and Zhu, Y.-P. Network analysis of the protein chain tertiary structures of heterocomplexes. Protein and Peptide Letters 2006, 13(4): 391-396.


    2. Wang, B., Chen, P., Huang, D.S., Li, J., Lok, T.M., and Lyu, M.R. Predicting protein interaction sites from residue spatial sequence profile and evolution rate. FEBS Letters 2006, 580 (2):380-384.


    1. Li, J., Huang, D., MacCallum, R., and Wu, X.-R. Characterizing human gene splice sites using evolved regular expressions. Proceedings of IEEE International Joint Conference on Neural Networks 2005, 1:493-498.