ArticleNPJ Parkinson's disease2022
Multi-modality machine learning predicting Parkinson's disease.
Article in NPJ Parkinson's disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
73 citing papers in PubMed, 4 syntheses or guidelines pooled it, 146 citations in OpenAlex.
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Polygenic Resilience Modulates the Penetrance of Parkinson Disease Genetic Risk Factors.Annals of neurology · 2022Pooled it
- Establishing gene regulatory networks from Parkinson's disease risk loci.Brain : a journal of neurology · 2022Pooled it
- Study of blood linear RNA nominates CD55 and DLD as early-stage biomarkers for Parkinson's disease.Brain : a journal of neurology · 2026Article
- Multimodal Electrophysiological Signals for Machine Learning-Aided Parkinson's Disease Diagnosis.Biosensors · 2026Article
- GLP-1 agonists in neurodegeneration: a multimodal biomarker-guided approach.Trends in molecular medicine · 2026Review
- A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Genome-wide association and population-tailored polygenic risk for Parkinson's disease in Taiwan.NPJ Parkinson's disease · 2026Article
- Plasma proteomics for Parkinson's disease classification: cross-cohort benchmarking of proteomic, transcriptomic, and multimodal models.NPJ Parkinson's disease · 2026Article
- M[Formula: see text]DGAT: Multi-view multi-scale dynamic graph attention network(GAT) based prediction of Parkinson's disease(PD) progression using whole-blood RNA sequencing data.Scientific reports · 2026Article
- Understanding Neurodegenerative Diseases From the -Omics Perspective: Lessons Learnt.Annals of neurology · 2026Review
- Accessible assessment of motor and cognitive symptoms in Parkinson's disease: integrating large datasets, machine-learning, and videoconferencing.NPJ digital medicine · 2026Article
- Artificial Intelligence Predicts GBA1 Mutated Status in Parkinson's Disease Patients.Movement disorders clinical practice · 2026Article
- Brain connectivity fingerprinting as a predictive biomarker of art therapy outcomes in Parkinson's disease.Research square · 2026Article
- Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease.PloS one · 2026Article
- Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson's disease.Frontiers in digital health · 2026Article
- Machine learning analysis of population-wide plasma proteins identifies hormonal biomarkers of Parkinson's disease.Frontiers in aging neuroscience · 2026Article
- Identification of genes associated with smell dysfunction in Parkinson's disease.Clinical parkinsonism & related disorders · 2026Article
- Dynamic context-aware multi-modal deep learning for longitudinal prediction of Parkinson's disease progression.Scientific reports · 2025Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors at 8 institutions in 5 countries.
Funding
Abstract
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.