Virtual Cell Can Simulate Cellular Growth Using Machine Learning

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.