MIT researchers have incorporated a new feature into machine-learning algorithms that improves biologists' ability to analyze huge amounts of data and identify potential new drugs.
Using the new approach, which allows computer models to account for uncertainty in the data they're analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis.
The method has previously been used by computer scientists but has not taken off in biology, says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory.
"This is a paradigm shift, and is absolutely how biological exploration should be done," she says.
Berger, assistant professor of biological engineering Bryan Bryson, and graduate student Brian Hie describe their work in "Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design," published in Cell Systems.
From MIT News
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