January 28, 2019Nearly 50 million people worldwide have Alzheimer’s disease or another form of dementia. While age is the greatest risk factor for developing the disease, researchers believe most Alzheimer’s cases occur as a result of complex interactions among genes and other factors. But those factors and the role they play are not known—yet.
In a new study, USC researchers used machine learning to identify potential blood-based markers of Alzheimer’s disease that could help with earlier diagnosis and lead to non-invasive ways of tracking the progress of the disease in patients.
The method was developed by USC computer science research assistant professor Greg Ver Steeg, a senior research lead at the USC Information Sciences Institute (ISI). Machine learning is a subset of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed.
“This type of analysis is a novel way of discovering
patterns of data to identify key diagnostic markers of
disease,” said team member Paul Thompson, the associate director of the
USC Mark and Mary Stevens Neuroimaging and Informatics
Institute and professor in the Keck School of Medicine at USC.
“In a very large database of health measures, it helped us
discover predictive features of Alzheimer’s disease that
nobody suspected were there.”
The study, “Uncovering Biologically Coherent Peripheral
Signatures of Health and Risk for Alzheimer’s Disease in
the Aging Brain,” appeared in Frontiers in
Aging Neuroscience, Nov. 28. The study authors are from the
USC Mark and Mary Stevens Neuroimaging and Informatics
Institute and the USC Information Sciences Institute.
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