Machine Learning Algorithm Predicts Cancer Drug Efficacy

A big part of personalized medicine in cancer is knowing ahead of time if a drug is likely to be effective or not. That's usually done by identifying actionable genetic mutations. But a team of researchers recently developed a potentially quicker and more consistent tool based on omics data: a machine learning algorithm that ranks drugs based on their anti-proliferative efficacy in cancer cells. Known as Drug Ranking Using Machine Learning (DRUML), the method was developed at Queen Mary University in London and is based on machine learning analysis of protein omics data in cancer cells. DRUML was created based on training responses of cancer cells to 412 cancer drugs to predict the most appropriate one to treat a particular cancer.