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.
Catherine Kim places top three at international student scientist competition
The entire Tatonetti Lab is proud to congratulate Catherine Kim on her top three placing at the Society for Science and Regeneron International Student Scientist Competition. Catherin was awarded the a Regeneron Young Scientist Award with a cash prize of $50,000 for her work in the Tatonetti Lab titled “Novel Prediction of Adverse Drug Reactions and Underlying Pathological Mechanisms via Hierarchical Classification.” As for what Catherin is up to next? Well she has accepted a spot at Brown where she will be begin her undergraduate academic career. All of us here in the lab are so excited to see what Catherine does next!
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.