It might be surprising to hear a tobacco giant described as a tech innovator. But Philip Morris researchers are pioneering new territory with a crowdsourced approach to checking the accuracy of life sciences data.

In partnership with computational biologists at IBM’s Watson Research Center, Philip Morris’s so-called sbv IMPROVER project creates open challenges to encourage scientists to augment traditional peer reviews of research data. On Monday, Philip Morris launched its Species Translation Challenge, which will award three $20,000 prizes to teams whose results best define how well rodent tests can predict human outcomes.

Similar competitions have emerged in the academic world, but sbv IMPROVER (short for “systems biology verification of industrial methodology for process verification in research” in case you were wondering) is the first that taps the crowd to verify industrial research. An initial challenge last year awarded $50,000 to two Wayne State University researchers who proved best at confirming genetic features that could be considered “diagnostic signatures” for particular diseases.

Why is a cigarette manufacturer sponsoring such competitions? “Our number one objective is to do something about our dangerous products,” says Philip Morris scientific communications director, Hugh Browne. (The company is known for its periodic candor about such matters, even as it continues to dominate the industry.) From heart disease to cancer to emphysema, the potential consequences of smoking are well known. But not every smoker suffers all or any of those health effects, suggesting that a combination of environmental and genetic factors lead to disease.

To understand precisely how smoking and chewing tobacco leads to complex interactions in a user’s biological systems, “Philip Morris is increasing its investments into systems biology,” Browne says. The company is looking at networks of genes, proteins, and biochemical reactions to identify the exact biological mechanisms perturbed by smoking.

But such biological data is notoriously complex to analyze. The profession as yet lacks any standard methodology for verifying results, and traditional peer-review methods have “struggled with the volume and complexity of the data,” according to Philip Morris.

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