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Learning to See

By Chris Edwards

Communications of the ACM, Vol. 61 No. 12, Pages 13-15

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How do you look for a needle in a haystack, when you are not sure what the needle looks like? This is the problem that faces scientists as they try to deal with increasingly complex datasets. One answer is to turn machine learning loose on the enormous volumes of data they have captured.

The problem of finding relevant data in genetic databases is one that Simon Roux, a researcher working at the U.S. Department of Energy's Joint Genome Institute, faced when investigating the role that an obscure and little-understood family of viruses plays in the environment.


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