AI may predict which viruses will spread from animals to humans.
According to the Centers for Disease Control and Prevention, three out of four emerging or new infectious diseases can be traced back to humans.
The Centers for Disease Control and Prevention (CDC) defines zoonoses as diseases caused by harmful germs like viruses, bacteria, fungi, and parasites transmitted from animal to human. A new study in Cell Reports shows that scientists used artificial intelligence (AI) and machine learning to predict the spread of viruses from animals to people. This is a breakthrough that could one day prevent pandemics and viral infections.
“Our model summarizes the known binding specificities of influenza virus, and we show that these predictions are extensible to other viruses such as coronaviruses and rotaviruses,” said Daniel Bojar, PhD., an assistant professor at the University of Gothenburg, Sweden. He was co-authored with John Quackenbush, Ph.D. at Harvard School of Public Health, and Rebekka Burkholz (postdoctoral researcher at Harvard School of Public Health).
Researchers used glycans (also known as carbohydrates), chains-link structures made up of single sugar molecules (monosaccharides), to create a new approach. Glycans, which are carbohydrate-based polymers, are made by all living organisms.
Researchers wrote that glycans are a particular class of biological molecules because they have a nonlinear, branching structure that allows them to perform various functions. These include protein folding and destruction; stress response; cell interactions; cell migration patterns; self/non-self discrimination; and microbiome composition, composition, and health.
They used AI deep learning to create a graph convolutional neural net (GCNN), which could learn a representation of glycans. The model was named SweetNet. Chart convolutional neural networks are used for many purposes, including computer vision, bioinformatics, and traffic detection.
The scientists wrote that SweetNet is a graph convolutional neural system that uses graph representation to facilitate computational understanding of glycobiology. “SweetNet explicitly reflects the nonlinearity of glycans and provides a framework for mapping any glycan sequence into a model.”
SweetNet’s glycan representations outperformed other models, according to the researchers. The SweetNet-based models for glycans are more efficient and require less training.
According to the researchers, SweetNet could train models 30% faster than their equivalent SweetTalk models. “SweetNet also performed better than SweetTalk in data efficiency, even though it used only a third of the dataset for training.”
The researchers reported that SweetNet is superior to other computational methods in predicting glycan characteristics on all tasks. SweetNet’s glycan representations are predictive of both environmental and organismal properties. We use glycan-focused machine learning to predict viral binding. This can be used to identify viral receptors. Scientists have made a significant step in using AI deep learning to glycobiology to identify glycans that could be used as antivirals to help prevent pandemics and viral infections.
Researchers used AI deep learning to create a graph convolutional neural net (GCNN), which could learn a representation of glycans. SweetNet’s glycan representations outperformed other models, according to the researchers. Scientists have made a significant step in using AI deep learning to glycobiology to identify glycans that could be used as antivirals to help prevent pandemics and viral infections.