Wellcome Trust partners with Sage Bionetworks to launch Wellcome Data Re-use Prizes


To learn more and submit your idea, visit the contest page.


A photo illustration of a person who's staring at a reflected screen of data and graphics.
Credit: istock. Prize submissions are likely to be a piece of code or analysis, plus a short description of the work.

Data re-use can generate new insights that in turn lead to vital health benefits. To stimulate and celebrate the innovative re-use of data, the Wellcome Trust has announced the launch of the Wellcome Data Re-use Prizes. With the support of Sage Bionetworks and its Synapse platform, the Wellcome Data Re-use Prizes will present two awards, each based on a topic of strategic importance to Wellcome: AMR surveillance and malaria.

The challenges create incentives for re-use and help form a community around the data.

“We are excited to partner with the Wellcome Trust to demonstrate how the re-use of public datasets can lead to better scientific insights,” said Dr. Larsson Omberg, Vice President for Systems Biology at Sage Bionetworks. “In addition to judging the reproducibility of the methods, we are encouraging people to work together by awarding extra points for new collaborations.”

Call for Entries

Wellcome is fully committed to ensuring that research outputs are made available to accelerate health benefits. This means that the researchers we support must maximize the availability of their data and other outputs with as few restrictions as possible.

Making data available in a timely and responsible way ensures other researchers can verify it, build on it, and use it to advance knowledge and make health improvements. But we don’t want to encourage data sharing for its own sake – we want the data that is shared by our researchers to be re-used by others to generate new insights and tools.

Wellcome Data Re-use Prizes

Entries have to generate a new insight, tool or health application from data available in an open data resource of Wellcome’s choosing.

There are two prizes, each based on a topic of strategic importance to Wellcome: AMR surveillance and malaria. The winner of each prize will get £15,000 (approx. $19,000 USD). Two runners-up will get £5,000 (approx. $6,400 USD).


To learn more and submit your idea, visit the contest page.


About Wellcome Trust: Wellcome exists to improve health for everyone by helping great ideas to thrive. We’re a global charitable foundation, both politically and financially independent. We support scientists and researchers, take on big problems, fuel imaginations, and spark debate. We remain true to the vision and values of our founder, Sir Henry Wellcome, a medical entrepreneur, collector and philanthropist. Our work today reflects the amazing breadth of Henry’s interests, and his belief that science and research expand knowledge by testing and investigating ideas.

About Sage Bionetworks: Sage Bionetworks is a nonprofit research organization that believes that open practices can help accelerate biomedical research. Founded in 2009, our interdisciplinary team of scientists and engineers work together to provide researchers access to technology tools and scientific approaches to share data, benchmark methods, and explore collective insights, all backed by Sage’s gold-standard governance protocols and commitment to user-centered design. Located in Seattle, Sage is supported through a portfolio of competitive research grants, commercial partnerships, and philanthropic contributions. Learn more at www.sagebionetworks.org.

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

The Parkinson’s Disease Digital Biomarker DREAM Challenge uses mobile sensor data to identify aspects of Parkinson’s disease severity based on measurements of movement 

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.

To identify the best methods for processing mobile sensor data, DREAM Challenges and Sage Bionetworks with support from the Robert Wood Johnson and Michael J. Fox Foundations have launched the Parkinson’s Disease Digital Biomarker DREAM Challenge. Using data collected through two Parkinson’s Disease mobile research studies, the goal of this challenge is to determine how best to use and mine mobile sensor data in order to distinguish gait and motor differences between Parkinson’s Disease patients and controls.

Are you a data scientist with expertise in signal processing or have an interest in mobile health research? Register to join the DREAM Challenge and help researchers identify better ways to use smartphones and remote sensing devices to monitor health and disease. The winning researcher teams with the best methods for processing sensor data will share the $25,000 prize.

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

Challenge is aimed at helping to refine cancer detection algorithms so they can be used in routine clinical practice

ARMONK, N.Y. – 2 JUNE 2017: IBM and Sage Bionetworks announced today that 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.

More than 120 independent teams of data experts from inside and outside the medical imaging field have participated in the challenge, which focused on developing predictive algorithms that reduce false-positive mammograms while maintaining or improving cancer detection. The goal is to enhance the predictive accuracy of algorithms so that they can be used in routine clinical practice.

In the first phase of the challenge, participants completed two tasks: They (i) developed a predictive algorithm that can analyze digital mammography images, (ii) and developed a predictive algorithm that can analyze both digital mammography images and clinical information.

Winning Teams

Yaroslav Nikulin, an engineer from the French imaging company Therapixel, and his team received top honors for their work on the first task and tied for first place in the second task. In the first task, they developed an algorithm with a predictive accuracy of 80.3 percent, which is 5 percent more accurate than the runner up. In the second task, Nikulin and his team developed an algorithm that was 80.4 percent accurate.

Tied for first place in the second task was a team led by Yuanfang Guan, Assistant Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. The group developed an algorithm with a predictive accuracy of 77.5 percent and outperformed the runner-up by more than 2 percent. Though the difference in accuracy between Guan’s and Nikulin’s teams was 2.9 percent, their performance was indistinguishable in the other metrics used to score the algorithms. Both winning teams used “Deep Learning,” one of the most advanced artificial intelligence techniques capable of analyzing and interpreting images.

Reducing False Positives

Mammograms are widely considered the most accessible and cost-effective breast cancer screening method. However, the American Cancer Society and the United States Preventive Services Task Force recently issued changes to recommendations for when women should start having mammograms and how often they should get them. The changes are due, in part, to the large number of false-positive mammograms. One in 10 women undergoing screening mammography is recalled for a diagnostic workup, though fewer than 5 percent of the recalled women will eventually be found to have cancer. Recalled patients often experience stress and additional medical costs, and some require interventions, including unnecessary biopsies. New algorithms may eventually be used by doctors to help them customize screening regimens for patients and identify women who would benefit from more or less frequent screening.

About the Challenge

Participating teams used hundreds of thousands of de-identified mammograms and clinical trial data provided by Kaiser Permanente Washington and the Icahn School of Medicine at Mount Sinai to create algorithms that can determine a woman’s cancer status in the 12 months following her mammogram.

Eight teams with the best algorithms will now move on to the community phase of the challenge, where they will be invited to add outside expert collaborators. They will also share their source code publicly, including other challenge participants, in an attempt to foster cooperative learning. In the community phase, finalists will work together to develop an algorithm that can fully match the accuracy of an expert radiologist.

“I am extremely pleased with the results of the competitive phase of the DM Challenge,” said Gustavo Stolovitzky, Director at IBM Research and Founder of the DREAM Challenges. “By providing powerful computational resources and making available what is, to the best of our knowledge, the largest public mammography dataset ever released, we empowered hundreds of data scientists to contribute to the solution in the fight against breast cancer. Moreover, the code and methods generated during the DM Challenge are now available for anybody interested in building on these results to help solve this important public health problem.”

“The innovation in this challenge stems not only from its final output—a set of robust models to aid clinicians in detecting breast cancer—but also the structure of the challenge itself. This challenge embodies a new paradigm for data sharing and cloud-hosted collaboration to tackle important questions in biomedicine,” said Dr. Justin Guinney of Sage Bionetworks. “By working together as a community of researchers and using the best tools of science and technology, we have advanced set the framework for clinicians in the field of breast cancer detection.”

If by the end of the community phase, the top eight teams—including Nikulin’s, Guan’s, and six others—can develop an algorithm that matches the expert radiologist performance of about 87.9 percent accuracy, they will receive a prize of up to $1 million.

The DM Challenge was born out of the White House’s Cancer Moonshot initiative and is funded in part by the Laura and John Arnold Foundation. It was designed by an organizing committee that includes IBM, Sage Bionetworks, Kaiser Permanente Washington, the Icahn School of Medicine at Mount Sinai, and the U.S. Food and Drug Administration. The challenge relied on the technological advances of Sage Bionetworks and IBM—Sage provided Synapse, a collaborative platform to host the challenge, as well as science and engineering expertise; IBM research teams in the United States, Israel, and Australia built the infrastructure for the challenge within the IBM Watson Health SoftLayer cloud and contributed further engineering and data science expertise.

Additional information about the DM Challenge is available here: https://www.synapse.org/Digital_Mammography_DREAM_Challenge

About DREAM Challenges

First conceived by IBM in 2006, DREAM Challenges have addressed objectives that range from predictive models for disease progression to developing models for cell signaling networks. Designed and run by a community of researchers, DREAM Challenges invite participants to propose solutions, fostering collaboration and building communities in the process. The DREAM Challenges community shares a vision of open collaboration to leverage the “wisdom of the crowd” to improve human health and sciences.

About IBM Research

For more than seven decades, IBM Research has defined the future of information technology, with more than 3,000 researchers in 12 labs located across six continents. Scientists from IBM Research have been awarded six Nobel prizes, a U.S. Presidential Medal of Freedom, ten U.S. National Medals of Technology, five U.S. National Medals of Science, and six Turing Awards. The teams have also included 19 inductees into the U.S. National Academy of Sciences and 20 inductees into the U.S. National Inventors Hall of Fame. For more information about IBM Research, visit www.ibm.com/research.

About Sage Bionetworks

Founded in 2009, Sage Bionetworks is a nonprofit biomedical research organization that promotes innovations in personalized medicine by enabling a community-based approach to scientific inquiries and discoveries. In pursuit of this mission, Sage Bionetworks has assembled an information commons for biomedicine supported by Synapse, an open compute space. The commons facilitates open research collaborations and innovative DREAM Challenges; it also empowers citizens and patients to share data and partner with researchers through Sage’s BRIDGE platform (https://developer.sagebridge.org/).

Media Contact:
Adrienne Sabilia
Media Relations, IBM Research

Sage Bionetworks:
Diane Gary
Sage Bionetworks
(206) 667-3038

[1] “Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer.” Investigative Radiology, 2017.https://www.ncbi.nlm.nih.gov/pubmed/28212138