A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease
Zhou, X. Parisi, L. Huang, W. Zhang, Y. Huang, X. Javid, F. Ma, R.
Zhou, X.
Parisi, L.
Huang, W.
Zhang, Y.
Huang, X.
Javid, F.
Ma, R.
Publication Date
2025-03
End of Embargo
Supervisor
Rights
© The Author(s) 2025. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/
licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For
commercial re-use, please contact journals.permissions@oup.com
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-02-18
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
Parkinson’s disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD’s diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD’s underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, proteomics, RNA sequencing, metabolomics, and dopamine transporter scan imaging, sourced from the Parkinson’s Progression Markers Initiative. Several model architectures were investigated and evaluated, including support vector machine, eXtreme Gradient Boosting, fully connected neural networks with concatenation and joint modeling (FCNN_C and FCNN_JM), and a multimodal encoder-based model with multi-head cross-attention (MMT_CA). The MMT_CA model demonstrated superior predictive performance, achieving a balanced classification accuracy of 97.7%, thus highlighting its ability to capture and leverage cross-modality inter-dependencies to aid predictive analytics. Furthermore, feature importance analysis using SHapley Additive exPlanations not only identified crucial diagnostic biomarkers to inform the predictive models in this study but also holds potential for future research aimed at integrated functional analyses of PD from a multi-omics perspective, ultimately revealing targets required for precision medicine approaches to aid treatment of PD aimed at slowing down its progression.
Version
Published version
Citation
Zhou X, Parisi L, Huang W et al (2025) A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease. Briefings in Bioinformatics. 26(2): bbaf088.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Article