Evidence map›Paper›PMID 38827071›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024

Learning Phenotypic Associations for Parkinson's Disease with Longitudinal Clinical Records.

Weishen Pan, Chang Su, Jacqueline R M A Maasch, Kun Chen, Claire Henchcliffe, Fei Wang

Abstract read

In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Weishen PanDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.

Chang SuDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.

Jacqueline R M A MaaschDepartment of Computer Science, Cornell Tech, Cornell University, New York, NY, USA.

Kun ChenDepartment of Statistics, University of Connecticut, Storrs, CT, USA.

Claire HenchcliffeDepartment of Neurology, University of California, Irvine, Irvine, CA, USA.

Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is associated with multiple clinical motor and non-motor manifestations. Understanding of PD etiologies has been informed by a growing number of genetic mutations and various fluid-based and brain imaging biomarkers. However, the mechanisms underlying its varied phenotypic features remain elusive. The present work introduces a data-driven approach for generating phenotypic association graphs for PD cohorts. Data collected by the Parkinson's Progression Markers Initiative (PPMI), the Parkinson's Disease Biomarkers Program (PDBP), and the Fox Investigation for New Discovery of Biomarkers (BioFIND) were analyzed by this approach to identify heterogeneous and longitudinal phenotypic associations that may provide insight into the pathology of this complex disease. Findings based on the phenotypic association graphs could improve understanding of longitudinal PD pathologies and how these relate to patient symptomology.

Identifiers

PMID38827071

PMCPMC11141836

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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.