ArticleBioMed research international2020
Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 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
- Roles of JAMM-family deubiquitinase STAMBPL1 in cellular homeostasis and human cancer.Molecular biology reports · 2026Review
- Plasma proteins and irritable bowel syndrome: A proteome-wide Mendelian randomization study.Medicine · 2026Article
- Hippocampus-targeted BDNF gene therapy to rescue cognitive impairments of Alzheimer's disease in multiple mouse models.Genes & diseases · 2026Article
- MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection.ArXiv · 2026Article
- Research on Prediction of Multiple Degenerative Diseases and Biomarker Screening Based on DNA Methylation.International journal of molecular sciences · 2025Article
- Use of Multiple Machine Learning Approaches for Selecting Urothelial Cancer-Specific DNA Methylation Biomarkers in Urine.International journal of molecular sciences · 2024Article
- Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.Scientific reports · 2023Article
- Article
- Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data.Computational and structural biotechnology journal · 2023Article
- Article
- Age-Related DNA Methylation in Normal Kidney Tissue Identifies Epigenetic Cancer Risk Susceptibility Loci in the ANKRD34B and ZIC1 Genes.International journal of molecular sciences · 2022Article
- Article
- Identification of Regulatory Factors and Prognostic Markers in Amyotrophic Lateral Sclerosis.Antioxidants (Basel, Switzerland) · 2022Article
- Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments. It is essential to identify potential gene biomarkers for AD pathology.
methodsDNA methylation expression data of patients with AD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially methylated sites were identified. The functional annotation analysis of corresponding genes in the differentially methylated sites was performed. The optimal diagnostic gene biomarkers for AD were identified by using random forest feature selection procedure. In addition, receiver operating characteristic (ROC) diagnostic analysis of differentially methylated genes was performed.
resultsA total of 10 differentially methylated sites including 5 hypermethylated sites and 5 hypomethylated sites were identified in AD. There were a total of 8 genes including thioredoxin interacting protein (TXNIP), noggin (NOG), regulator of microtubule dynamics 2 (FAM82A1), myoneurin (MYNN), ankyrin repeat domain 34B (ANKRD34B), STAM-binding protein like 1, ALMalpha (STAMBPL1), cyclin-dependent kinase inhibitor 1C (CDKN1C), and coronin 2B (CORO2B) that correspond to 10 differentially methylated sites. The cell cycle (FDR = 0.0284087) and TGF-beta signaling pathway (FDR = 0.0380372) were the only two significantly enriched pathways of these genes. MYNN was selected as optimal diagnostic biomarker with great diagnostic value. The random forests model could effectively predict AD.
conclusionOur study suggested that MYNN could be served as optimal diagnostic biomarker of AD. Cell cycle and TGF-beta signaling pathway may be associated with AD.
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