The researchers offered TopCoder what they thought would be an impossible goal: to develop a predictive algorithm that was an order of magnitude better than either a custom solution developed by Arnaout or the NIH's standard approach (MegaBLAST) and that could scale up to mounting data demands. To do this, they had to first reframe the problem, translating it so that it could be accessible to individuals not trained in computational biology. Among the 84 solutions produced by the TopCoder Community, 16 were an improvement over MegaBLAST with one over 970 times faster than either and was produced during a two week long competition costing just $6,000.
"This is a proof-of-concept demonstration that we can bring people together not only from different schools and different disciplines, but from entirely different economic sectors, to solve problems that are bigger than one person, department or institution," said Eva Guinan , HMS associate professor of radiation oncology at Dana-Farber Cancer Institute and director of the Harvard Catalyst Linkages Program. "Given how complicated the immune system is, this has been a particularly formidable biological problem, and building tools for solving it has been hard and time-consuming. We were stunned by the power of these results and their potential application."
"In a way, the immune system is really the dark matter of biology," said Ramy Arnaout, assistant professor of pathology at Beth Israel Deaconess Medical Center. "We have all this sequence data, and there's no good way to figure out what it's doing. Not only did the best entries achieve truly superior performance, but also this kind of crowdsourcing has the potential to be a general solution for a whole class of problems in biology. No single university or institution has the bandwidth and reso
|SOURCE TopCoder, Inc.|
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