The information age is also the age of information overload. Companies, governments, researchers, and private citizens are accumulating digital data at an unprecedented rate, and amid all those quintillions of bytes could be the answers to questions of vital human interest: What environmental conditions contribute most to disease outbreaks? What sociopolitical factors contribute most to educational success? What player statistics best predict a baseball team’s win-loss record?

There are a host of mathematical tools for finding possible relationships among data, but most of them require some prior knowledge about what those relationships might be. The problem becomes much harder if you start with a blank slate, and harder still if the data sets are large. But that’s exactly the problem that researchers at MIT, Harvard University, and the Broad Institute tackle in a paper published in December 2011 in the journal Science.

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