October 2, 2014
  • Question. Collaborate. Contribute.

DREAM Challenges pose fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, our challenges invite participants to propose solutions — fostering collaboration and building communities in the process. Expertise and institutional support are provided by Sage Bionetworks, along with the infrastructure to host challenges via their Synapse platform. Together, we share a vision allowing individuals and groups to collaborate openly so that the  “wisdom of the crowd” provides the greatest impact on science and human health.
Sage

FEATURED CONTENT

The DREAM Challenge team discuss what DREAM Means to them in this new DREAM Challenges Video produced at the 2016 conference in Phoenix.  

DREAM Conference Info

10th Annual RECOMB/ISCB Conference on Regulatory and Systems Genomics, with DREAM Challenges Now in its tenth year!  Join the DREAM the Community at the Memorial Sloan Kettering Cancer Center in New York City on Tue., Nov. 21 for our Annual Conference and learn the results of this years Challenges.
July 24, 2017

DREAM Challenge Community competition launches to learn how to use smartphone and wearable sensors to monitor health

Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for remote monitoring of health and disease – particularly for diseases affecting motor function such as Parkinson’s. While there are many projects that have successfully collected sensor data from people in the real-world setting, researchers still have a poor understanding of what the data can tell us about health. (Read more.)

June 2, 2017

IBM and Sage Bionetworks announce winners of first phase of DREAM Digital Mammography Challenge

IBM and Sage Bionetworks announced today the winners of the first phase of its DREAM Digital Mammography (DM) Challenge have developed algorithms that had 5% fewer false-positive errors in breast cancer screenings than recently published state of the art computerized methods1. This 5 percent improvement could potentially lead to less anxiety and unnecessary procedures for an estimated two million women per year in the United States and could help reduce costs associated with follow-up exams and biopsies. (Read more)

June 2, 2017

Can Machine Learning Help Improve Accuracy in Breast Cancer Screening?

Breast Cancer is the most common cancer in women. It is estimated that one out of eight women will be diagnosed with breast cancer in their lifetime. The good news is that 99 percent of women whose breast cancer was detected early (stage 1 or 0) survive beyond five years after diagnosis, leading countries around the world to implement breast cancer screening programs for early detection. (Read more)