ReviewFrontiers in aging neuroscience2023
Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs.
Review in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Salivary protein macromolecules as diagnostic and therapeutic biomarkers linking oral health and neurodegenerative diseases-emerging mechanisms and future perspectives.Inflammopharmacology · 2026Review
- Utility of Baseline Pathological, Neuroimaging and Clinical Markers for Prognosis in Early Parkinson's Disease.Journal of geriatric psychiatry and neurology · 2026Article
- Towards biomarker-based diagnosis of Parkinson disease.Nature reviews. Neurology · 2026Review
- Prompting and Fine-Tuning Large Language Models for Parkinson Disease Diagnosis: Comparative Evaluation Study Using the PPMI Structured Dataset.JMIR medical informatics · 2026Article
- Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.European journal of neurology · 2025Observational
- A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease.Briefings in bioinformatics · 2025Article
- A lightweight cerebrospinal fluid biomarker-based model for first-diagnosis prediction of Parkinson's disease: model development, external validation, and local deployment.Frontiers in aging neuroscience · 2025Article
- Transcriptomics profiling of Parkinson's disease progression subtypes reveals distinctive patterns of gene expression.Journal of central nervous system disease · 2025Article
- An integrative network approach for longitudinal stratification in Parkinson's disease.PLoS computational biology · 2025Article
- Accelerating Parkinson's Disease drug development with federated learning approaches.NPJ Parkinson's disease · 2024Review
- Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis.NPJ Parkinson's disease · 2024Article
- White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.Quantitative imaging in medicine and surgery · 2024Article
- Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.
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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.