The collaborative initiative’s first challenge, the Diagnostic Signature Challenge, has already been completed after being launched in March of this year, with members of the global scientific and academic community invited to identify diagnostic signatures in four disease areas: psoriasis, multiple sclerosis, chronic obstructive pulmonary disease and lung cancer. The initial aim of the Diagnostic Signature Challenge was to verify whether it is possible to extract robust diagnostic signatures for each of the four diseases under investigation. If it was established that diagnostic signatures can be identified, the next goal was to identify the best diagnostic signature for each disease together with its associated discovery algorithm.  

 

The research modality that IMPROVER employs “confronts scientists with huge challenges to ensure that their conclusions are accurate, robust and capable of translating through to innovative policies, processes and products,” says Dr. Gustavo Stolovitzky, manager of functional genomics and systems biology at the IBM Computational Biology Centre. “The outcome of the Diagnostic Signature Challenge shows us that IMPROVER has the potential to significantly influence how systems biology can be verified in industrial contexts in the years to come.”  

 

The next challenge, to be launched in the second quarter of 2013, will be the Species Translation Challenge.

 

“With our first challenge, we wanted to start as simple as we could with something so complex, and confirm that our approach works, since people do all kinds of things looking at data like this, but not usually in a systematic fashion,” Sprengel says. “The next steps will be to move to a more qualitative rather than quantitative point, and determine translatability from model systems to human cell lines. The next level up from that will be to look at more predictive goals.”  

 

Using a crowdsourcing approach was important in getting beyond peer review constraints but still retaining much of the value of what peer review provides, Sprengel and Browne maintain.  

 

“In the first challenge, there were teams that performed very well in some areas but not as well in others,” Sprengel notes. “Our intention is to get the best overall performers and evaluate what they can offer to try and improve the approaches as much as we can.” “If you look at the 54 teams that submitted entries for the first challenge, they used subtly different approaches,” Browne continues, “and that’s the ‘wisdom of the crowds’ from which you can take elements of one solution and combine it with another and probably have a better solution overall.”