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.
Introducing the new nSIDES.io
The Tatonetti Lab has just launched a new nSIDES.io site to serve as the home for our growing list of resources on drug safety. The new site will serve as the official resource launching platform for OnSIDES, OffSIDES, TwoSIDES, ManySIDES, and the newest member KidSIDES. Read more about these drug safety resources and how to access the code and data at http://nsides.io.
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!
Payal featured by Computer Science Department
TLab member Payal Chandak was featured by the Department of Computer Science for her work on characterizing the differences in adverse drug effect reporting between men and women. Payal invented a method for data mining the FDA’s Adverse Event Reporting System to look for biases along reported sex that resulted in significantly different safety signals. Through a combination of machine learning and large-scale statistical analysis she was able to identify 20,817 sex risks which were consistent with known pharmacogene expression differences.