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Scientists have created a virtual yeast cell model that can learn from real-world behaviors, a key step in utilizing artificial intelligence in healthcare to diagnose diseases.
A team of researchers from the University of California San Diego has developed what they called a “visible” neural network that enabled them to build DCell—a machine learning model of a functioning brewer’s yeast cell that is commonly used in basic research.
Machine learning systems are built on a neural network that consist of layers of artificial neurons that are tied together by seemingly random connections between neurons. The systems “learn” by fine-tuning those connections.
In DCell, the researchers amassed all knowledge of cell biology in one place and created a hierarchy of the cellular components.
For machine learning to be useful and trustworthy in healthcare, practitioners need to understand how a system arrives at a decision, said Trey Ideker, PhD, University of California San Diego School of Medicine and Moores Cancer Center professor, in a statement.
“It seems like every time you turn around, someone is talking about the importance of artificial intelligence and machine learning,” Ideker said. “But all of these systems are so-called ‘black boxes.’ They can be very predictive, but we don’t actually know all that much about how they work.”
Via Charles Gerth
