ArticleInternational journal of general medicine2024
Integrated Transcriptomic and Machine Learning Analysis Identifies
Article in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Unraveling the core: hub genes bridging familial and sporadic Parkinson's disease.Journal of neurology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons. This study aims to discover potential new genetic biomarkers for PD. Methods: Transcriptome data from a total of 56 patients with PD and 61 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database. Differential gene expression (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms (LASSO, Random Forest, SVM-RFE) were employed to identify pivotal PD-associated genes. Additionally, RT-qPCR experiments were conducted to validate our findings in clinical specimens. Functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed to explore the functional and pathway mechanisms of the identified genes in PD. Molecular docking studies revealed potential small-molecule drug targets for the key genes. Results: The results from the three machine learning algorithms identified Conclusion: These findings underscore
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Identifiers
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Registered trials
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