Imagine unleashing the power of artificial intelligence to automate a critical component of biomedical research, expediting life-saving research in the treatment of almost every disease from rare disorders to the common cold. This could soon be a reality, thanks to the 2018 Data Science Bowl® in which, for the very first time, participants trained deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup—and without human intervention. Algorithms developed in this competition are projected to save researchers hundreds of thousands of hours of effort per year.
Identifying the cells’ nuclei is the starting point for most analyses because most of the human body’s 30 trillion cells contain a nucleus full of DNA, the genetic code that programs each cell. Identifying nuclei allows researchers to identify each individual cell in a sample, and by measuring how cells react to various treatments, researchers can understand the underlying biological processes at work.
By participating, you can work within a team to:
Bring hope faster—Automating the process will shorten research times and bring cures to market sooner, helping future patients faster
Help researchers understand DNA—Opening a window into the behavior of DNA will help scientists discover how diseases function
Speed new drugs—Identifying nuclei will allow for more efficient drug testing, shortening the approximately 10 years it takes for each new drug to come to market
What will participants do?
Teams will create a computer model that can identify a range of nuclei across varied conditions. By observing patterns, asking questions, and building a model, participants will have a chance to push state-of-the-art technology farther.
The 2018 Data Science Bowl® competition brought together nearly 18,000 global participants, the most ever for the Data Science Bowl. Collectively, they submitted more than 68,000 algorithms and worked an estimated 288,000 hours to automate the vital, but time-consuming, process of nuclei detection. Identifying nuclei allows researchers to find each individual cell in a sample and, by measuring how cells react to various treatments, understand the underlying biological processes at work.
“These solutions represent a paradigm shift in the way microscopy images are processed in biomedical research and will make research more accurate and efficient,” said Dr. Anne Carpenter, director of the Imaging Platform at Broad Institute of MIT and Harvard. The next step being investigated is to create a user-friendly and open-source software that biomedical researchers can begin using in their day-to-day work.
“By identifying nuclei quickly and accurately, the algorithms developed in this competition can free up biologists to focus on other aspects of their research, shortening the approximately 10 years it takes for each new drug to come to market and, ultimately, improving quality of life,” said Ray Hensberger, a Booz Allen Hamilton principal. “This year’s Data Science Bowl was fascinating because nuclei detection is crucial to biomedical research, but detection methods require time-consuming biologist oversight. Until now, there have never been any deep learning models available that can identify nuclei across multiple experimental setups and testing conditions.”
For this year’s Data Science Bowl, Broad Institute of MIT and Harvard, a non-profit biomedical and genomics research institute, provided participants with data from thousands of nuclei from a wide variety of imaging experiments. Using this data, participants created algorithms that can identify nuclei in any cell image, thereby expediting the detection process and allowing biologists to conserve time for other efforts.
“We’re thrilled that this year’s Data Science Bowl convened thousands of the world’s top minds and rallied them around a pressing scientific challenge,” said Anthony Goldbloom, CEO, Kaggle. “In just 90 days, participants submitted over 68,000 algorithms, which is nearly 280 percent more than last year’s competition. The large numbers of forum posts and kernels resulting from this year’s competition demonstrate that we have fostered a growing community that is willing to share and learn together, which is truly inspiring.”
Victor Durnov, Alexander Buslaev, and Selim Seferbekov, an international team from Russia and Germany who have competed against each other in previous crowdsourcing competitions but formed a team with the specific goal of winning this year’s competition.
Got a big idea for us?
Ready to re-invent the future?
See potential ahead?
In our first contest, we dove deep with a microscopic lens to improve ocean health. In our last, we went on a life-saving mission to spot nuclei to diagnose killer diseases. In each case, we did what couldn’t be done before: bring the awesome creativity and capability of data scientists to open the doors to new approaches.
What should we tackle next? If you have ideas, let us hear from you!
We’re in the hunt for the next big problem to solve—a problem with the potential to change the world. If selected, the power of the entire data science community will be harnessed against it.
Contact us to submit your ideas or email DataScienceBowl@bah.com. Include an overview of the problem, your contact information, a brief description of the data, and where it can be obtained.