ArticleBioinformation2021
Identification of marker genes in Alzheimer's disease using a machine-learning model.
Article in Bioinformation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 26 citations in OpenAlex.
- The GPCR Connection: Linking Alzheimer's Disease and Glioblastoma.Journal of cellular and molecular medicine · 2026Review
- AD diagnosis model based on fusion of heterogeneous brain imaging and genomic data.Frontiers in neuroscience · 2026Article
- Membrane-Type 5 Matrix Metalloproteinase (MT5-MMP): Background and Proposed Roles in Normal Physiology and Disease.Biomolecules · 2025Review
- Biomarker Identification for Alzheimer's Disease Using a Multi-Filter Gene Selection Approach.International journal of molecular sciences · 2025Article
- DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis.Frontiers in neuroscience · 2025Article
- Canopy FGF signaling regulator 3 affects prognosis, immune infiltration, and PI3K/AKT pathway in colon adenocarcinoma.World journal of gastrointestinal oncology · 2024Article
- Potential Plausible Role of Stem Cell for Treating Depressive Disorder: a Retrospective Review.Molecular neurobiology · 2024Review
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- A review and analysis of key biomarkers in Alzheimer's disease.Frontiers in neuroscience · 2024Review
- Exploring orphan GPCRs in neurodegenerative diseases.Frontiers in pharmacology · 2024Review
- Review
- Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets.Scientific reports · 2023Review
- Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.Briefings in bioinformatics · 2023Article
- Plausible Role of Stem Cell Types for Treating and Understanding the Pathophysiology of Depression.Pharmaceutics · 2023Review
- MiRNA Differences Related to Treatment-Resistant Schizophrenia.International journal of molecular sciences · 2023Article
- Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.
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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.