Professor Eytan Ruppin publishes in Cell Systems

On March 23, 2016, Professor Eytan Ruppin of Computer Science and The Center for Bioinformatics and Computational Biology published a new article in Cell Systems entitled "Metabolic Network Prediction of Drug Side Effects" with Itay Shaked, Matthew A. Oberhardt, Nir Atias, and Roded Sharan (all of Tel Aviv University).  

The team of researchers are examining ways to quickly determine side effects from medications using machine learning methods. They use medical informatics resources and a human genome-scale metabolic model to present  the array of model-based phenotype predictors (AMPP) to identify these probems which can include intersitial nephritis and other pyramid disorders.  Drug side affects "levy a massive cost on society through drug failers, morbidity, and mortality cases every year..[.]" The group has determined that AMPP predicts, rather substantially, (AUC > 0.7) for >70 drug side effects. AMPP also is a better indicator than a previous biochemical structure-based method for the prediction of metabolic side effects. 

This work is significant as it will help to reduce side effects that are metabolic in nature--particularly as scientists develop new drug treatments for various diseases.