Academics who studied the same data were able to predict patients' responses to therapy with 70 percent accuracy, Dampier says. Of the 109 individuals or teams who decided to enter, it was a college dropout from Baltimore, Internet marketing whiz Chris Raimondi, 39, who topped the pile, writing an algorithm that predicted outcomes with 78 percent accuracy.
Raimondi's algorithm was best at linking specific mutations in HIV to how well a patient will respond to drug therapy.
"The approach of having a public competition is completely innovative," says Richard Harrigan, an associate professor at the University of British Columbia who studies HIV drug efficacy and participated in the contest. "A number of academic approaches use some of the same data sets to address the question, so, in theory, this could be useful."
However, Harrigan says, because Dampier's data set drew from dated patient information, from as far back as the 1980s, the contest results aren't clinically useful for those patients, some of whom may have died. These methods could potentially help doctors personalize patient therapy.
The point of the exercise wasn't to find a cure for HIV, Dampier says. It was a statement about how a more inclusive approach would help solve health-care problems.
"I'm hoping the people who come into these competitions are people who have no biases like [those of academics] because they'll have the most interesting view of the data," says Dampier, assistant director at Drexel University's Center for Integrated Bioinformatics. "In judo, we call it having a 'beginner's mind.' "
Crowd-sourcing could take the quest for solutions "in a novel direction," says Robert Gross, an HIV expert at the University of Pennsylvania. "It's very clever."
