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.
Translational Bio Year-in-Review 2023
That’s a wrap on the AMIA Informatics Summit 2023 TBI Year-in-Review! Thank you to everyone for attending, thanks to the authors for publishing, and most of all, thank you to the 2023 TBI Year-in-Review Committee who triaged over 1,100 papers and scored over 400. Final count was 26 papers plus 10 shout outs and 2 pieces of brain candy. Slides are linked below.
TLab moves to LA 🏖️
I’m excited to share that I have joined the faculty at Cedars-Sinai in Los Angeles as Vice Chair of Computational Biomedicine and Associate Director of the Cancer Center! My group will continue to advance state-of-the-art of drug side effect and DDI research, biomedical data mining and AI, and translational bioinformatics. In addition, these leadership positions will bring opportunities to implement DS/AI into the clinical and research workflow at one of our nation’s top hospitals.
I am so grateful to Columbia DBMI and all that we have accomplished over the past 10 years. Read the full story of heartfelt highlights.
OnSIDES v2.0.0 Released
The second major release of OnSIDES (On-label SIDE effectS resource) is now available at nsides.io and github.com/tatonetti-lab/onsides. This version contains significant model improvements and a simpler data structure. The result is significantly improved performance metrics (F1=90, AUROC=92, AUPR=95) at extracting adverse reactions from the FDA structured product labels. Thanks to all the TLab members that contributed to this release, including Cindy, Dhvani, Pietro, and Jason!