Deep learning for biology


By Sarah Webb

Four years ago, scientists from Google showed up on neuroscientist Steve Finkbeiner’s doorstep. The researchers were based at Google Accelerated Science, a research division in Mountain View, California, that aims to use Google technologies to speed scientific discovery. They were interested in applying ‘deep-learning’ approaches to the mountains of imaging data generated by Finkbeiner’s team at the Gladstone Institute of Neurological Disease in San Francisco, also in California.

Deep-learning algorithms take raw features from an extremely large, annotated data set, such as a collection of images or genomes, and use them to create a predictive tool based on patterns buried inside. Once trained, the algorithms can apply that training to analyse other data, sometimes from wildly different sources.

The technique can be used to “tackle really hard, tough, complicated problems, and be able to see structure in data — amounts of data that are just too big and too complex for the human brain to comprehend”, Finkbeiner says.

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