DREAM 9: An acute myeloid leukemia prediction big data challenge

Demo of Algorithms & Clinical Visualization In 2014, there will be 18,860 new cases of acute myeloid leukemia (AML), and 10,460 deaths from AML. There is urgency in finding better treatments for this type of leukemia, as only about a quarter of the patients diagnosed with AML survive beyond 5 years. The goal of the 2014 DREAM 9 Acute Myeloid Leukemia (AML) Outcome Prediction Challenge is to harness the power of crowd-sourcing to speed the pace of analyzing a high-dimensional proteomics and clinical dataset for AML. The DREAM (Dialog for Reverse Engineering of Assessments & Methods) community consists of diverse computational researchers, biomedical scientists and clinicians who apply their skills to solve a biomedical problem. In this year’s DREAM AML Outcome Challenge, participants worldwide compete to develop the best predictive models of AML clinical outcome based on clinical attributes and proteomics. Results of the Challenge include predictive clinical models that surpass current standards; new algorithms to visualize high-dimensional clinical outcome data; and insight into markers of AML and potential new cancer drug targets. In this short demo, we will present on some of the methods behind this crowd-sourced biomedical data challenge.