ReviewNPJ Parkinson's disease2020
Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease.
Review in NPJ Parkinson's disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 55 citations in OpenAlex.
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease.PloS one · 2026Article
- The gut-brain axis in Alzheimer's and Parkinson's diseases: a comparative perspective.Frontiers in microbiomes · 2026Review
- Disrupted genes and pathways in schizophrenia: a robust analysis of the brain and blood.BMC psychiatry · 2025Article
- DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson's disease subtypes.BMC bioinformatics · 2025Article
- TransGeneSelector: using a transformer approach to mine key genes from small transcriptomic datasets in plant responses to various environments.BMC genomics · 2025Article
- Transcriptomic analysis reveals associations of blood-based A-to-I editing with Parkinson's disease.Journal of neurology · 2024Article
- Preclinical translational platform of neuroinflammatory disease biology relevant to neurodegenerative disease.Journal of neuroinflammation · 2024Article
- scHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering.Briefings in bioinformatics · 2024Article
- Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges.Journal of molecular and cellular cardiology plus · 2023Review
- Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.Scientific reports · 2023Article
- Applications of Machine Learning to Diagnosis of Parkinson's Disease.Brain sciences · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer's disease.Computational and structural biotechnology journal · 2023Article
- Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease.NPJ Parkinson's disease · 2022Article
- Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial Intelligence Predictor for Alzheimer's Disease Trained on Blood Transcriptome: The Role of Oxidative Stress.International journal of molecular sciences · 2022Article
- Article
- Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson's disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.
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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.