ArticleFrontiers in molecular neuroscience2022
Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases.
Article in Frontiers in molecular neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson's disease.NPJ Parkinson's disease · 2026Article
- PGVDA: a pathway-aggregated genetic dosage framework for interpretable disease classification using machine learning.Briefings in bioinformatics · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Short-Chain Fatty Acids as a Therapeutic Strategy in Parkinson's Disease: Implications for Neurodegeneration.Cellular and molecular neurobiology · 2025Review
- Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.Scientific reports · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Predictive genetic panel for adult asthma using machine learning methods.The journal of allergy and clinical immunology. Global · 2024Article
- The 2023 Report on the Proteome from the HUPO Human Proteome Project.Journal of proteome research · 2024Review
- Myasthenia Gravis: Novel Findings and Perspectives on Traditional to Regenerative Therapeutic Interventions.Aging and disease · 2023Article
- Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.Frontiers in microbiology · 2023Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.
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Registered trials
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