University of Rochester Medical Center researchers are now reaching for new tools to both improve care and identify new therapies for Parkinson’s disease. These tools include algorithms, machine learning, computer simulations and mobile technologies.
The research involved Charles Venuto, an assistant professor in the URMC Department of Neurology and the Center for Health + Technology and GNS Healthcare, and was funded by the Michael J. Fox Foundation for Parkinson’s Research and the National Institute of Neurological Disorders and Stroke.
The scientists mined large data sets compiled by the Parkinson’s Progression Markers Initiative which has collected biological samples and clinical data from hundreds of individuals with the disease.
In a departure from traditional research approaches, the team turned over the vast quantities of genetic, clinical, and imaging profiles compiled by the PPMI study to a machine learning and simulation program. As the computer program analyzed the data, it was also “learning” by constantly refining and modifying its criteria and algorithms as it sifted through the information looking for patterns and associations.
The study identified a mutation in a gene that, together with a second gene and demographic factors, could identify patients with faster motor progression of Parkinson’s. The finding, if confirmed, could ultimately help clinicians refine care and help researchers more precisely understand how individual patients may respond to experimental therapies. Parkinson’s disease is a progressive neurological disorder that erodes an individual’s control over their movements and speech.
Other researchers at UR also have been busy, adding to the growth in the application of data-driven technologies to biomedical research. For instance, URMC neurologist Ray Dorsey, director of the Center for Health + Technology , has been at the forefront of this transformation, the medical center said. Dorsey has long been a pioneer in expanding access to Parkinson’s care via telemedicine. In 2015, Dorsey – in collaboration with Sage Bionetworks – helped develop an iPhone app which allows patients with Parkinson’s disease to track their symptoms in real time and share this information with researchers.
Gaurav Sharma, a professor at UR’s Department of Electrical and Computer Engineering, is working with wearable sensors to track the progression of Parkinson’s and Huntington’s diseases. Eshan Hoque, an assistant professor in the Department of Computer Science, is developing analytical tools that scan videos of patients to help diagnose early stage Parkinson’s.
“The volume of data we are now generating is astronomical,” Dorsey said. “In the past we would collect data from a patient once every six months, now we have sensors that are sampling data 10 times per second. So as opposed to spending a lot of effort to gather a small amount of data, now with very little effort we are generating huge amounts of data.”