Skip to main content

Using Machine Learning on Longitudinal Data from Multiple Cohorts to Determine Biological Subtypes and Prediction Scores for Parkinson’s Disease

Study Rationale: We used to think of Parkinson's disease (PD) as a single disorder. However, individuals with PD can be very different in terms of their clinical characteristics and the biological processes that underlie their disease. We aim to apply artificial intelligence to datasets collected from large patient cohorts to propose distinct subtypes of the disease.

Hypothesis: We hypothesize that our analyses will allow us to identify subtypes that will differentiate individuals with PD based on clinical and biological information.

Study Design: We will begin by standardizing the data contained in two publicly available PD datasets. Next, we will run machine-learning models to generate PD subtypes. Finally, we will determine whether these subtypes accurately distinguish individuals in terms of their biomarker profiles, genetic contributions and clinical presentation.

Impact on Diagnosis/Treatment of Parkinson’s disease: We hope to help in developing new treatments for PD.

Next Steps for Development: If our approach is successful, it can be used to better select individuals for clinical trials and to determine the most effective treatment protocols.


  • Artur Francisco Schumacher Schuh, MD, PhD

    Porto Alegre-RS Brazil

  • Marco Antônio De Bastiani, PhD

    Porto Alegre Brazil

  • Rodrigo Coelho Barros, PhD, MSc, BSc

    Porto Alegre-RS Brazil

  • Eduardo Rigon Zimmer, BPharm, MSc, PhD

    Porto Alegre-RS Brazil

Discover More Grants

Search by Related Keywords

Within the Same Funding Year

We use cookies to ensure that you get the best experience. By continuing to use this website, you indicate that you have read our Terms of Service and Privacy Policy.