ArticleExperimental and therapeutic medicine2017
Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network.
Article in Experimental and therapeutic medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Candidate SNP Markers Significantly Altering the Affinity of the TATA-Binding Protein for the Promoters of Human Genes Associated with Primary Open-Angle Glaucoma.International journal of molecular sciences · 2024Article
- RGIE: A Gene Selection Method Related to Radiotherapy Resistance in Head and Neck Squamous Cell Carcinoma.Current radiopharmaceuticals · 2024Article
- Biomarker Genes Discovery of Alzheimer's Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia.Brain sciences · 2022Article
- Investigation of Key Signaling Pathways Associating miR-204 and Common Retinopathies.BioMed research international · 2021Article
- The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat.G3 (Bethesda, Md.) · 2020Article
- Unified feature association networks through integration of transcriptomic and proteomic data.PLoS computational biology · 2019Article
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
The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including
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Registered trials
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