Here at Medgadget, we have previously covered efforts to crowdsource ideas for developing medical technology and innovation. For example, we have seen programs that crowdsource retinal connectome mapping and protein folding.
Now, as reported in a recent issue of Nature Biotechnology, a unique combination of leaders from Harvard Medical School, Harvard Business School, and London Business School have demonstrated that a crowdsourcing platform discovered in the commercial sector can solve a complex immunological problem quicker that traditional biology algorithms, such as NIH’s BLAST, at a fraction of the cost.
The researchers partnered with TopCoder, a crowdsourcing platform with 450,000 algorithm specialists and software developers, to identify a program that can analyze the vast number of genes and gene mutations that build antibodies and T cell receptors. Using a prize-based contest, they identified 16 computer algorithms that were more accurate and up to 1,000 times faster than BLAST. The team released the top five performing code submissions under an open source license, allowing other researchers to use the information to possibly devise new biologic drugs and other pharmaceuticals. One possible application could lie in controlling autoimmune disease.
Led by Dr. Eva Guinan, HMS associate professor of radiation oncology at Dana-Farber Cancer Institute and director of the Harvard Catalyst Linkages Program, the researchers utilized the different skills of the multidisciplinary team to achieve this success. According Guinan:
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…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.
This is just another example of the power of crowdsourcing in medicine. It further illustrates the business and economic potential of such a solution. We look forward to more applications of crowdsourcing in the future.
Check out the press release: Solving Big-Data Bottleneck