Crowdsourcing solves complex bio-problems much faster

Last Updated: Fri, Feb 08, 2013 10:50 hrs

Washington, Feb 8 (IANS) Scientists have teamed up with business innovators to pioneer a crowdsourcing platform that can solve a complex biological problem and data bottlenecks much faster than conventional approaches -- and at a fraction of the cost.

Partnering with TopCoder, a crowdsourcing platform with a global community of 450,000 algorithm specialists and software developers, researchers from Harvard Medical and Business Schools and London Business School identified a programme that can analyse vast amounts of data, especially on genes and gene mutations that build antibodies.

Since the immune system takes a limited number of genes and recombines them to fight a seemingly infinite number of invaders, predicting these genetic configurations has proven a massive challenge, with few good solutions.

The programme identified through this crowdsourcing experiment succeeded with an unprecedented level of accuracy and remarkable speed, the journal Nature Biotechnology reports.

"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," said Eva Guinan, associate professor of radiation oncology at Dana-Farber Cancer Institute, and director, Harvard Catalyst Linkages Program.

"This study makes us think about greater efficiencies in academic research," said Karim Lakhani, associate professor in the technology and operations management unit at Harvard Business School, according to a Harvard statement.

"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.

Co-authors on the study included Po-Ru Loh (Massachusetts Institute of Technology), Lars Backstrom (TopCoder), Carliss Baldwin (Harvard), Eric Lonstein (HBS), Mike Lydon (TopCoder) and Alan MacCormack (Harvard).

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