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Duke University: Machine learning predicts behavior of biological circuits

Duke University researchers have devised a machine learning approach to modeling the interactions between complex variables in engineered bacteria that would otherwise be too cumbersome to predict, the university announced.
The they created algorithms are generalizable to many kinds of biological systems.
 
The researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture, according to the article. The system worked 30,000 times faster than the existing computational model.
 
To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers. Then they used it to solve a second biological system that is computationally demanding in a different way, showing the algorithm can work for disparate challenges.
 
The results appear online on September 25 in the journal Nature Communications.
 
“This work was inspired by Google showing that neural networks could learn to beat a human in the board game Go,” said Lingchong You, professor of biomedical engineering at Duke. You is a BMES member.

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