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Early detection and intervention are crucial for slowing PD progression. Understanding PD\u2019s diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.1093\/bib\/bbaf088","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T10:54:06Z","timestamp":1741604046000},"source":"Crossref","is-referenced-by-count":16,"title":["A novel integrative multimodal classifier to enhance the diagnosis of Parkinson\u2019s disease"],"prefix":"10.1093","volume":"26","author":[{"given":"Xiaoyan","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Biology, Shenzhen MSU-BIT University , Longcheng Street, Shenzhen 518115, Guangdong ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5865-8708","authenticated-orcid":false,"given":"Luca","family":"Parisi","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Tutorantis, 5 South Charlotte Street, Edinburgh EH2 4AN ,","place":["United Kingdom"]}]},{"given":"Wentao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Biology, Shenzhen MSU-BIT University , Longcheng Street, Shenzhen 518115, Guangdong ,","place":["China"]}]},{"given":"Yihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biology, Shenzhen MSU-BIT University , Longcheng Street, Shenzhen 518115, Guangdong ,","place":["China"]}]},{"given":"Xiaoqun","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Biology, Shenzhen MSU-BIT University , Longcheng Street, Shenzhen 518115, Guangdong ,","place":["China"]}]},{"given":"Mansour","family":"Youseffi","sequence":"additional","affiliation":[{"name":"Department of Engineering and Informatics, University of Bradford , Richmond Road, Bradford BD7 1DP ,","place":["United Kingdom"]}]},{"given":"Farideh","family":"Javid","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, University of Huddersfield , Queensgate, Huddersfield HD1 3DH ,","place":["United Kingdom"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2495-4787","authenticated-orcid":false,"given":"Renfei","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biology, Shenzhen MSU-BIT University , Longcheng Street, Shenzhen 518115, Guangdong ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"2025031010535718600_ref1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3390\/biology9050103","article-title":"Clinical features of Parkinson\u2019s disease: the evolution of 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