ArticleScientific reports2023
Blood biomarker-based classification study for neurodegenerative diseases.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- A bibliometric analysis of the immune system and cognitive impairment: trends from 1985 to 2024.Frontiers in aging neuroscience · 2025Pooled it
- The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.Frontiers in neuroscience · 2024Pooled it
- BRPtools: An AutoML-Powered web platform for multiclass disease prediction from bulk blood RNA-seq data.Molecular therapy. Nucleic acids · 2026Article
- A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.Biomedical engineering and computational biology · 2026Review
- Enhancing Early Diagnosis: Multimodal AI Approaches for Neurodegenerative Diseases.Journal of biotechnology and biomedicine · 2026Article
- The Effect of Naturally Acquired Immunity on Mortality Predictors: A Focus on Individuals with New Coronavirus.Biomedicines · 2025Article
- Music-induced physiological markers for detecting Alzheimer's disease using machine learning.Frontiers in aging neuroscience · 2025Article
- Differences in Blood and Cerebrospinal Fluid Between Parkinson's Disease and Related Diseases.Cellular and molecular neurobiology · 2024Review
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Graph Neural Networks for Parkinson's Disease Monitoring and Alerting.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer's (AD) and Parkinson's disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer's (ROC AUC = 0.810) and Parkinson's (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture.
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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.