The challenge drew 733 participants, of whom 122 (17%) submitted software code. This group of submitters, drawn from 69 countries, included roughly half (44%) professionals with the remainder being students at various levels. None were academic or industrial computational biologists, and only five described themselves as coming from either R&D or life sciences in any capacity. The 122 TopCoder members submitted 654 submissions yielding 89 different approaches to the problem. Collectively, participants averaged 5.4 submissions each. Participants reported spending an average of 22 hours developing solutions, for a total of 2,684 hours of development time. Sixteen of the submissions outperformed the accuracy (77%) of the traditionally developed custom solution and 30 outperformed the NIH MegaBLAST benchmark for accuracy (72%). A total of eight submissions achieved an 80% accuracy score, which is very near the theoretical maximum for the dataset.
"We're excited to see that ideas from economics and management fields can be so productively applied to medical research," said Kevin Boudreau , assistant professor of strategy and entrepreneurship at London Business School. "This progress is heartening, particularly in view of the computational challenges we face in understanding so many diseases. We hope this provides a model of how social science and medical researchers can collaborate to solve real-world problems that matter to people."
According to Karim Lakhani , associate professor in the Technology and Operations Management Unit at Harvard Business School, it is not only the world of basic biomedical research that can benefit from this project, but any organization that is facing significant data analytics and computational challenges. "Our research with Harvard Catalyst and the NASA Tournament Lab initiative points
|SOURCE TopCoder, Inc.|
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