ArticlePloS one2020
Identifying and predicting Parkinson's disease subtypes through trajectory clustering via bipartite networks.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- LACE-UP: An ensemble machine-learning method for health subtype classification on multidimensional binary data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cross-modal fusion of brain imaging and clinical data for Parkinson's disease progression prediction.PloS one · 2025Article
- Decision tree-based approach to robust Parkinson's disease subtyping using clinical data of the Michael J. Fox Foundation LRRK2 cross-sectional study.Frontiers in artificial intelligence · 2025Article
- Transcriptomics profiling of Parkinson's disease progression subtypes reveals distinctive patterns of gene expression.Journal of central nervous system disease · 2025Article
- Unraveling progression subtypes in people with Huntington's disease.The EPMA journal · 2024Article
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Predicting Ambulatory Capacity in Parkinson's Disease to Analyze Progression, Biomarkers, and Trial Design.Movement disorders : official journal of the Movement Disorder Society · 2023Observational
- Two-year clinical progression in focal and diffuse subtypes of Parkinson's disease.NPJ Parkinson's disease · 2023Article
- Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.NPJ Parkinson's disease · 2022Article
- Stroke recovery phenotyping through network trajectory approaches and graph neural networks.Brain informatics · 2022Article
- The collective vs individual nature of mountaineering: a network and simplicial approach.Applied network science · 2022Article
- A Systematic Review of Parkinson's Disease Cluster Analysis Research.Aging and disease · 2021Review
- Comprehensive subtyping of Parkinson's disease patients with similarity fusion: a case study with BioFIND data.NPJ Parkinson's disease · 2021Article
- Molecular Subtyping and Outlier Detection in Human Disease Using the Paraclique Algorithm.Algorithms · 2021Article
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Authors and funding
4 authors.
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Abstract
Chronic medical conditions show substantial heterogeneity in their clinical features and progression. We develop the novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes and 2) early prediction of subtype/disease progression patterns. TPC is an easily generalizable method that identifies subtypes by clustering patients with similar disease trajectory profiles, based not only on Parkinson's Disease (PD) variable severity, but also on their complex patterns of evolution. TPC is derived from bipartite networks that connect patients to disease variables. Applying our TPC algorithm to a PD clinical dataset, we identify 3 distinct subtypes/patient clusters, each with a characteristic progression profile. We show that TPC predicts the patient's disease subtype 4 years in advance with 72% accuracy for a longitudinal test cohort. Furthermore, we demonstrate that other types of data such as genetic data can be integrated seamlessly in the TPC algorithm. In summary, using PD as an example, we present an effective method for subtype identification in multidimensional longitudinal datasets, and early prediction of subtypes in individual patients.
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