Data-Driven Precision Pharmacology

We are making drugs safer through the analysis of data. Everyday millions of us or our loved ones take medications to manage our health. We trust in these prescriptions to improve our lives and give us hope for a healthier future. Often, however, these drugs have harmful side effects or dangerous interactions. Adverse drug reactions are experienced by millions of patients each year and cost the healthcare industry billions of dollars. In the Tatonetti Lab we use advanced data science methods, including artificial intelligence and machine learning, to investigate these medicines. Using emerging resources, such as electronic health records (EHR) and genomics databases, we are working to identify for whom these drugs will be safe and effective and for whom they will not. Browse our databases, contribute to our projects, and join us on this journey to make precision pharmacology a realty.

Calculated Risk by Shraddha Chakradhar in Nature Medicine

January 23, 2019

The Tatonetti Lab’s work is featured in the latest issue of Nature Medicine in “Calculated Risk” by Nature staff writer, Shraddha Chakradhar. Read about the moment of discovery that propelled the lab into the field of drug safety, pharmacovigilance, and drug-drug interactions here.

 

Featured on Australian Radio

July 23, 2018

ABC (Austrialian Broadcasting Company) interviews Dr. Tatonetti for their Health Report podcast. We talk data science, electronic health records, and the importance of maintaining patient privacy when conducting large scale analyses. Click to listen.

 

Tatonetti Lab gets the cover of Cell Magazine

June 15, 2018

On the cover: Vast amounts of medical records are stored in hospitals and clinics around the world. Digitalization of these records has made them available for secondary use in research of human disease and treatment. In this issue of Cell, Polubriaginof et al. (1692-1704) use these records to study disease heritability. The cover image is an artistic representation of the digital transformation process of paper medical records that enables this study.