ArticleBioMed research international2022
Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 57 citations in OpenAlex.
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 2025Article
- Using systems biology and drug repositioning approaches to discover FDA-approved drugs candidates for endometriosis treatment.PloS one · 2025Article
- Identification and experimental validation of KMO as a critical immune-associated mitochondrial gene in unstable atherosclerotic plaque.Journal of translational medicine · 2024Article
- Identification of SV2C and DENR as Key Biomarkers for Parkinson's Disease Based on Bioinformatics, Machine Learning, and Experimental Verification.Journal of molecular neuroscience : MN · 2024Article
- Exploration of ferroptosis and necroptosis-related genes and potential molecular mechanisms in psoriasis and atherosclerosis.Frontiers in immunology · 2024Article
- Association ofCell journal · 2023Article
- Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation.Scientific reports · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Parkinson's Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms.Brain sciences · 2023Article
- Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms.Open life sciences · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the main cause of this disease. The purpose of this research was to discover genes involved in PD development. Methods: In this study, the microarray dataset (GSE22491) provided by GEO was used for further analysis. The Limma package under R software was used to examine and assess gene expression and identify DEGs. The DAVID online tool was used to accomplish GO enrichment analysis and KEGG pathway for DEGs. Furthermore, the PPI network of these DEGs was depicted using the STRING database and analyzed through the Cytoscape to identify hub genes. Support vector machine (SVM) classifier was subsequently employed to predict the accuracy of genes. Result: PPI network consisted of 264 nodes as well as 502 edges was generated using the DEGs recognized from the Limma package under the R software. Moreover, three genes were identified as hubs: Conclusion: According to the findings of the study, the 3 hub genes
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Registered trials
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