Jingjing Li is an assistant professor in the Department of Neurology and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco.
Li’s research is focused on large-scale genomic analyses. He develops machine-learning techniques to integrate multi-omics data, evolutionary insights and electronic health records, which directly translates genomic findings into clinical discoveries.
In particular, his group focuses on addressing two key challenges that have prevented us from so far deriving a deep understanding of the genetic architecture of complex human diseases: (i) elucidating the role of noncoding regulatory mutations that affect gene expression in human diseases, and (ii) mapping the hidden structure of biological pathways where the seemingly heterogeneous mutations in human diseases converge.
Li’s group is currently studying autism spectrum disorder (ASD), which is one of the most challenging conditions to understand due to the extreme phenotypic and genetic heterogeneity. Li and his colleagues integrate genomic and proteomic approaches to reveal key biological pathways affected in autism. They also develop machine-learning approaches to integrate ASD genomes, brain-specific epigenomes and phenotypic data to study the molecular and behavioral impacts of the altered regulatory network.