We use genomic approaches to identify genetic elements in spontaneous preterm birth, and analyze their population differentiation. Jingjing Li. University of California, San Francisco. Each genomic locus has its own evolutionary trajectory, and we trace the evolutionary origin of disease-associated mutations back to archaic humans, primates, as well as distantly related vertebrate species. We also perform digital signal processing to analyze these longitudinal data to capture subtle signals that will enable us to detect diseases before symptoms emerge. PLoS Genetics 2017 13(4): e1006689. Join to Connect. affecting ~18% in African Americans, 6% in East Asians, and ~10% on average across the nation. Jingjing Li. Jingjing Li, PhD researches large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, electronic health records, as well as digitized clinical traits from imaging and wearable sensor readouts. Integrating genomic information, health records and socioeconomic data, we aim to solve the stark health disparity problem , and achieve precision management for human pregnancies. Genome Research 2014, 24(5):775-785. Health is a descriptive concept, which is often defined as “free from disease”. American journal of … Jingjing Li: Mon, Wed, Fri, 2-4p: details BMS 270/BMI 219: Fundamentals of Microbiome Research: Peter Turnbaugh: Mon-Fri, 2:00-4:00p (5/17-5/28) details: BMS 270: Current Approaches to Quantitative Proteomics in Biology: Arun Wiita: Mon, Wed, Fri, 9:30a-12p: details: BMS 270: Advanced Topics on Unlocking Human Autoimmunity: Mark Anderson To request changes to a department listing please open a ticket at help.ucsf.edu, or contact the IT Service Desk at (415) 514-4100. Molecular Systems Biology 2014, 10:774. We use multi-omics approaches to delineate the convergent molecular networks in neurological diseases, and develop deep learning frameworks to directly predict clinical outcomes from personal genomes. Chao Li, Shuo Wang, Jiun-Lin Yan, Rory J Piper, Hongxiang Liu, Turid Torheim, Hyunjin Kim, Jingjing Zou, Natalie R Boonzaier, Rohitashwa Sinha, Tomasz Matys, Florian Markowetz, Stephen J Price. Molecular mechanisms and novel therapeutic targets for retinal degenerations. The candidates will also develop close collaborations with both UCSF and Stanford research groups including many leading scientists, ... CV, a short summary of your qualifications and research interests, and contact information for three references to Dr. Jingjing Li. Weizmann Institute of Science - Department of Molecular Genetics. Both bioinformatics and … Jingjing Li Assistant Professor at UCSF San Francisco Bay Area 148 connections. Li, J. et al. Jingjing Li Assistant Professor at UCSF Mountain View, California Higher Education. Gene-Environment Interaction in the Era of Precision Medicine. Gene-environment interaction in the era of precision medicine. Li, J. et al. Research summary: Large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, electronic health records, as well as digitized clinical traits from imaging and wearable sensor readouts. Exploiting the determinants of stochastic gene expression in S. cerevisiae for genome-wide prediction of expression noise. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. @ Eli and Edythe Broad Center of Regeneration, Building Data-Driven Frameworks to Solve Complex Diseases, Personal Genomes + Clinical Records + Individual Lifestyles + Emerging Technologies. Using iPSC-derived cells to identify pathogenic non-coding mutations We also actively work on congenital heart diseases for a direct translation from genome sequences to clinical manifestations. Exome sequencing of neonatal blood spots identifies genes implicated in bronchopulmonary dysplasia. Jingjing Li's profile, publications, research topics, and co-authors. In addition to using clinical records to model gene-environment interaction, we also leverage imaging and wearable sensor technologies to longitudinally digitize clinical traits. Jingjing Li, PhD From Big Data to Big Mind: Building Data-Driven Frameworks to Solve Complex Diseases. Assistant Professor Emmy (Jingjing) Li Graduate Student at UCSF Boston, Massachusetts 36 connections. Tel: 415-502-2572 We are particularly interested in understanding the evolutionary trade-offs associated with local adaptation events during modern human evolution and migration, and these evolutionary "costs" will help us understand the origin of human diseases. Li , J. et al. October 17, 2019 By Karen. Li, J. et al. This site is running Profiles RNS version v2.12.0-792-gbe55c6ce on PROFILES-PWEB01. However, our genomic analysis of complex diseases is often challenged by mutational heterogeneity, where different patients often carry different sets of clinical mutations. All of the faculty associated with the BMS graduate program are listed below. View the profiles of professionals named "Jingjing - Jessie Li" on LinkedIn. There are 3,500+ professionals named "Jingjing Li", who use LinkedIn to exchange information, ideas, and opportunities. Li, J. et al. This concept has been the cornerstone of today’s precision medicine. Chen Eitan. (Note that publications are often cited in additional ways that are not shown here.) Report this profile; Experience. University of California, San Francisco. Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. Cancer Research 2020, 80:4644–54.. 32. Experience. Assistant Professor, Neurology. We therefore use multi-omic profiling strategies to construct biological networks, and map genomic mutations onto biological networks to identify disease-associated pathways. We study the structural organization of genes on biological networks, and use ChIP-Seq to delineate the epigenome landscapes of the developing brain and heart in humans. 2The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, The Parker Institute for Cancer Immunotherapy, The Bakar Computational Health Sciences Institute, Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, California. Speaker: Jingjing Li, PhD, Assistant Professor of Neurology, UCSF Weill Institute for Neurosciences The Basic Research Monthly Meeting focused on prostate cancer research across UCSF. Natural selection has differentiated the progesterone receptor among human populations. Li, J. et al. Gene-environment interaction in the era of precision medicine. Jingjing Li, PhD. It's free! Click on a faculty name to view a detailed description of an individual research program, contact information, recent publications and links to other relevant websites. Primary Thematic Area: Fetal de novo mutations and preterm birth. We integrate personal genome baselines and individual lifestyles for precision health management. The main theme of our research is large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, electronic health records, as well as digitized clinical traits from imaging and wearable sensor readouts. Cell 2018, 174: 1361-1372. His research direction involves integrating the genomic and clinical big data in cancer research using machine learning approaches. We develop machine learning algorithms to define personal genome baselines for disease occurrences, and identify actionable lifestyle elements whose modification could compensate for genetic risk. All of the faculty associated with the BMS graduate program are listed below. We previously demonstrated the RNA binding specificity of human Argonaute proteins, and are now elucidating the mutational effects on RNA secondary structure and protein-RNA interaction. Our research aims to build a data-driven framework to quantitatively define health. Cheng received Ph.D. from the National University of Singapore working on elucidating the regulation of pre-mRNA splicing in breast cancers. Eran Hornstein. Large-scale analysis of disease genomes We use cookies to ensure a smooth browsing experience. Daniel Lim, MD, PhD studies the role of chromatin remodeling factors in the regulation of neural stem cell fate. A deep learning framework identifies pathogenic noncoding somatic mutations from personal prostate cancer genomes. Contact Us. Join to Connect. Core. Integrating genomic data and digitized physiological and behavioral profiles, we aim to detect diseases before symptoms emerge. He did his postdoc research at Stanford Genetics as a Banting Fellow, and later received … Altmetrics Details 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. Email: jingjing.li@ ucsf.edu Address: 35 Medical Center Way, RMB 934H, San Francisco, CA 94143. With comprehensively profiled individual lifestyles and physiologies, we aim to extend the classical P=G+E model to a Bayesian framework, where we stratify individuals based on personal genome baselines, followed by identifying lifestyle elements whose modification could improve clinical outcome. View Jingjing Li’s full profile. View the profiles of professionals named "Jingjing - Jessie Li" on LinkedIn. We use these technologies to derive quantitative traits for fine-mapping of genetic elements. Li, J. et al. Address: 35 Medical Center Way, RMB 934H, San Francisco, CA 94143. The scope of this informal forum is geared toward work in basic science: lab bench and computation. We recently showed that these seemingly heterogeneous mutations are in fact convergent onto common pathways. Cell 2019, 177(1):38-44. The main theme of our research is large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, electronic health records, as well as digitized clinical traits from imaging and wearable sensor readouts. Jingjing Li , PhD Jingjing Li, PhD We build machine learning models to define personal genome baselines for the risk of cardiovascular diseases, and quantify the contribution from lifestyle adjustments to risk reduction. These cohort-based approaches cannot directly identify individual pathogenic noncoding mutations from personal cancer genomes. PNAS 2010, 107(23): 10472-10477. jingjing.li@ucsf.edu. Large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, health records, and digitized clinical traits from imaging and wearable sensor readouts. Kevin P. Kenna 139 connections. Large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, health records, and digitized clinical traits from imaging and wearable sensor readouts. Li, J., et al. We perform ChIP-Seq and ATAC-Seq in iPSC-derived cells to delineate the genome-wide chromatin architecture in disease-related cell types, and build machine learning algorithms to identify non-coding mutations (SVs, indels and CNVs) that disrupt local and global chromatin architecture. Clinical phenotyping using emerging technologies Decoding the Genomics of Abdominal Aortic Aneurysm. UCSF Profiles is managed by the UCSF Clinical and Translational Science Institute (CTSI), part of the Clinical and Translational Science Award program funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health (NIH). Jingjing Li, PhD From Big Data to Big Mind: Building Data-Driven Frameworks to Solve Complex Diseases. Cell 2019, 177(1):38-44. Read our theory paper: Gene-environment interaction in the era of precision medicine. We use multi-omics approaches to delineate the convergent molecular networks in neurological diseases, and develop deep learning frameworks to directly predict clinical outcomes from personal genomes. Weizmann Institute of Science - Department of Molecular Genetics. Deepak Lamba, PhD. Li, J. et al. Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival. University of California, San Francisco. The fields of bioinformatics and computational biology at UCSF aim to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. We combine genomic analysis and iPSC techniques to model the mutational impacts on disease onset and progression. Therefore, although most somatic mutations are localized in the noncoding cancer … Cell Systems 2015, 1(5): 361-374. American Journal of Human Genetics (AJHG) 2018,103:1-13. Trends in Genetics 2013, 29(2): 116-124. View the profiles of professionals named "Jingjing Li" on LinkedIn. UCSD Profiles is managed by the UC San Diego Altman Clinical and Translational Research Institute (ACTRI). Li J, Pan C, Zhang S, Spin JM, Deng A, Leung LLK, Dalman RL, Tsao PS, Snyder M, Li J, Hong X, Mesiano S, Muglia LJ, Wang X, Snyder M, Stevenson DK, Shaw GM, Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, Urban A, Hallmayer J, Snyder M, ©2021 The Regents of the University of California, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, Bakar Computational Health Sciences Institute (BCHSI), Parker Institute for Cancer Immunotherapy. In addition to our clinical research, we have keen interests in many fundamental questions in genome sciences. University of California, San Francisco (UCSF) - Department of Neurology. At UCSF, his research is focused on cancer genomics. Natural Selection Has Differentiated the Progesterone Receptor among Human Populations. Our current understanding of disease genomes is largely limited to coding sequences, and the non-coding regions, accounting for ~98.5% of the human genome, have been largely unexplored for their contribution to disease etiologies. Preterm birth affects 15 million pregnancies worldwide every year, and is the leading cause for neonatal morbidity and mortality. University of California, San Francisco. Jingjing Li, PhD From Big Data to Big Mind: Building Data-Driven Frameworks to Solve Complex Diseases The main theme of our research is large-scale analysis of disease genomes by integrating multi-omics data, evolutionary insights, electronic health records, as well as digitized clinical traits from imaging and wearable sensor readouts. Identification of human neuronal protein complexes reveals biochemical activities and convergent mechanisms of action in autism spectrum disorders. Wang, C. and Li, J. There are 3,100+ professionals named "Jingjing - Jessie Li", who use LinkedIn to … By continuing we assume you accept the use of cookies. jingjing.li@ucsf.edu. Decoding the genomics of abdominal aortic aneurysm. Identifying mRNA sequence elements for target recognition by human Argonaute proteins. Dr. Li’s predoctoral training was in engineering and machine learning, and later received his PhD in Molecular Genetics from the University of Toronto. microRNA regulatory variation in human evolution. 33. Li J, Pan C, Zhang S, Spin JM, Deng A, Leung LLK, Dalman RL, Tsao PS, Snyder M Natural Selection Has Differentiated the Progesterone Receptor among Human Populations. Jingting Li's profile, publications, research topics, and co-authors. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Li, J. et al. Jingjing Li, PhD From Big Data to Big Mind: Building Data-Driven Frameworks to Solve Complex Diseases. This condition has a strong genetic component, and is hallmarked by a strong population disparity, e.g. Speaker: Jingjing Li, PhD, Assistant Professor of Neurology, UCSF Weill Institute for Neurosciences The Basic Research Monthly Meeting focused on prostate... 12:30pm Virtual Event University of California, San Francisco (UCSF) - Department of Neurology. We also investigate many understudied areas in genomic medicine, with a focus on genomic alterations at the RNA level. Because many complex human diseases have a strong genetic component, it has been long anticipated that once we can sequence one’s personal genome, we would be able to predict the person’s clinical outcome. Because these approaches allow large-scale and quantitative analyses of biological phenomena and data obtained from many disciplines, they can ask questions and achieve unique insights not imaginable before the genomic era. 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