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.
TLab dives into SARS-CoV-2 Research
As a (mostly) computational lab, we count ourselves lucky to continue our work without too much disruption during the pandemic. It is probably because of this situation and the fact that we all are part of a community hit hard by the virus, that we are feeling extra motivated to do what we can to help. These past two weeks have been big for the lab. We now have three manuscripts on SARS-CoV-2 research out for review. These include a paper that replicates a previous finding associating blood groups with infection status, a study that identifies new clinical and genetic risk factors for SARS-CoV-2 outcomes, and a paper that investigates the role of ACE inhibitor exposure on infection rates.
These works were made possible by a lot of hard work from Michael, Undina, Vijendra, and Phyllis. 💪
2020 TBI Year-in-Review features COVID-19 Data Science Research
Thank you to for the HUGE turnout for my 2020 TBI Year-in-Review talk! I had a wonderful time hosting my first YiR. To recap, we reviewed over 260 papers and highlighted 21 amazing translational bioinformatics papers plus an additional 11 COVID papers. The slides are available for download here.
Congratulations to Dr. Thangaraj!
Phyllis Thangaraj, an MD/PhD student in the lab, successfully defended her dissertation entitled “Electronic health record-derived phenotyping models to improve genomic research in stroke.” Her work explores the use of machine learning methods to transform noisy binary indicators of disease status common in electronic health records to quantitative descriptors. Dr. Thangaraj’s central hypothesis is that these quantitative descriptors can provide more nuance to disease definitions that will enhance genetic studies that rely on these data to discovery new genomic variants. Her two papers are currently in submission and can be read as pre-prints here and here.