Screen shot of the Prescience interpretable machine learning system for predicting hypoxemia in patients undergoing surgeryBiology is fast becoming an information science, with large databases and sophisticated algorithms now essential tools in the field. Allen School faculty and students collaborate with researchers in biology and medicine on a wide range of computational problems that will ultimately enable us to understand and augment complex biological systems. The Allen School and the University of Washington are at the forefront of exciting innovations at the intersection of computation and biology to advance scientific discovery, develop new diagnostics and therapeutics, and usher in a new era of personalized medicine. Learn more about our work by visiting the pages of our individual researchers and labs.

Also check out our work in Molecular Programming & Synthetic Biology and Computational Neuroscience & Neuroengineering.

AI for bioMedical Sciences (AIMS) Lab

Members of the AIMS Lab develop explainable artificial intelligence for applications in health care and the life sciences. Working in partnership with biomedical researchers and clinicians in the UW School of Medicine and external institutions, the team aims to improve our understanding of the biology underlying diseases such as cancer and Alzheimer's; enable physicians to target disease treatments based on a patient's individual molecular profile; and to provide useful, interpretable predictions for a range of conditions and risk factors to improve patient outcomes.

Mostafavi Lab

Researchers in the Mostafavi Lab advance machine learning and statistical methods to explore complex biological processes related to human health and disease, such as the relationship between genetic variation and immune response, the mechanisms of psychiatric disorders, and the causes of rare genetic diseases in children. The lab develops computational tools for enabling researchers to combine association evidence across multiple types of molecular/genomics data, such as gene expression and genotype data, and to disentangle meaningful correlations from spurious ones. Members work closely with colleagues in a variety of fields, including immunology, neuroscience, genetics and psychiatry.