Computational Medicine/Health

Overview

Our facuFrom Data to Healthlty develop computational methods and frameworks that translate biological data into clinical insights and therapeutic advances. This group addresses critical challenges at the intersection of genomics, biomedicine, and healthcare—from predicting disease risk and interpreting clinical variants to designing personalized treatment strategies and understanding the molecular basis of human disease. Research spans developing data-driven algorithms to analyze single-cell molecular atlases for studying immune-metabolic dysregulation in cancer and autoimmunity, pioneering methods for clinical genome interpretation and newborn screening that enable diagnosis years earlier than traditional approaches, organizing the Critical Assessment of Genome Interpretation (CAGI) to establish standards for variant interpretation, investigating microbiome-dependent metabolite signaling in kidney homeostasis and disease using mass spectrometry and chemoinformatics, developing frameworks for precision medicine that account for individual genetic variation, and creating computational approaches for understanding gene regulation through alternative splicing and nonsense-mediated mRNA decay.

The impact of our research is direct and tangible: improving diagnostic accuracy for rare and complex diseases (including discovering novel SCID-causing genes through advanced genome analysis), identifying patients most likelyto benefit from specific immunotherapies and targeted treatments, enabling earlier disease detection and prevention through genomic newborn screening, deciphering how diet-derived microbiome metabolites modulate human physiology, and accelerating the development of new treatments by integrating computational biology with experimental validation. By combining expertise in single-cell genomics, immunology, statistics, machine learning, structural biology, and clinical knowledge, our researchers bridge the gap between computational innovation and patient care. With collaborations across UCSF Medical Center, Chan Zuckerberg Biohub, Berkeley's Bioengineering and Public Health programs, and the broader Bay Area biomedical community, this work creates computational tools and insights that are actively shaping the future of precision medicine and improving human health outcomes.

Primary Faculty

  • Steven Brenner, Professor, Departments of PMB, MCB, and Bioengineering
  • Leah Guthrie, Assistant Professor, Department of Bioengineering
  • Allon Wagner, Assistant Professor, Departments of EECS and MCB

Secondary Faculty

  • Sandrine Dudoit, Professor, Department of Statistics and Division of Biostatistics
  • Daniel Fletcher, Professor, Department of Bioengineering
  • Haiyan Huang, Professor, Department of Statistics
  • Priya Moorjani, Associate Professor, CCB and Department of MCB
  • Yun S. Song, Professor, Departments of EECS and Statistics
  • Peter Sudmant, Associate Professor, Department of Integrative Biology
  • Bin Yu, Professor, Departments of Statistics and EECS

Affiliated Faculty