Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach
The AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder.
Authors
- Guangwu Lin (ORCID: https://orcid.org/0000-0001-5085-2111)
- Liangyun Hu
- Weidong Gu (ORCID: https://orcid.org/0000-0002-3014-5839)
- Shengdong Nie (ORCID: https://orcid.org/0000-0001-7825-4455)
- Yifeng Yang (ORCID: https://orcid.org/0000-0002-3639-5767)
- Y Xie (ORCID: https://orcid.org/0000-0002-9527-1422)
- Yang Chen (ORCID: https://orcid.org/0000-0002-5660-6349)
Institutions
- University of Shanghai for Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- Fudan University (CN)
- Ruijin Hospital (CN)
- Huadong Hospital (CN)
- Taian City Central Hospital (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-02-04
- DOI
- https://doi.org/10.3389/fnagi.2025.1510192
- Citations
- 9
- Primary Topic
- RNA regulation and disease
- Type
- article
- Field-Weighted Citation Impact
- 7.25