ArticleDisease models & mechanisms2022
Deep phenotyping for precision medicine in Parkinson's disease.
Article in Disease models & mechanisms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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Who cites it
13 citing papers in PubMed, 18 citations in OpenAlex.
- Multi-omics profiling of chronic immune-mediated skin diseases: SKINERGY protocol and strategic evaluation.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Observational
- Seed Amplification Assays for Parkinson's Disease: A Review of α-Synuclein Assays in Body Fluids and Tissues.Journal of neurochemistry · 2026Review
- A framework of digital biomarkers for neurodegenerative diseases.Nature reviews bioengineering · 2026Article
- Wearable-sensor based walking and non-walking measures as progression markers in early to mid-stage Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Data-driven subtyping of early Parkinson's disease via mutual cross-attention fusion of EEG and dual-task gait features.NPJ Parkinson's disease · 2026Article
- GWAS for Periodontitis Phenotypes Using Multi-Ancestry All of Us Research Platform.medRxiv : the preprint server for health sciences · 2025Article
- Systematic review and consensus conceptual model of meaningful symptoms and functional impacts in early Parkinson's Disease.NPJ Parkinson's disease · 2025Article
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Fox Insight at 5 years - a cohort of 54,000 participants contributing longitudinal patient-reported outcome, genetic, and microbiome data relating to Parkinson's disease.Scientific data · 2024Article
- Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Emerging perspectives on precision therapy for Parkinson's disease: multidimensional evidence leading to a new breakthrough in personalized medicine.Frontiers in aging neuroscience · 2024Review
- Genetic variance in human disease - modelling the future of genomic medicine.Disease models & mechanisms · 2022Article
- Precision medicine for Parkinson's disease: The subtyping challenge.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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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.