THU271 Identification Of Candidate Biomarkers For Type 1 Diabetes Mellitus By Bioinformatics Analysis Of Pooled Microarray Gene Expression Datasets In Gene Expression Omnibus
Bibliographic record
Abstract
Abstract Disclosure: K. Feng: None. W. Chen: None. Background: Type 1 diabetes (T1DM) is a serious threat to childhood life and has a complicated pathogenesis. Currently, molecular mechanisms of T1DM remain largely unclear. The aim of this study was to identify the candidate genes in T1DM by integrated bioinformatics analysis. Methods: Transcriptomic datasets (GSE156035) in the GEO database were analyzed for differentially expressed genes (DEGs) using the R statistical language. The differentially expressed genes (DEGs) were identified, and the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A protein - protein interaction (PPI) network was applied to screen out the candidate genes. Results: The results revealed that 273 DEGs of the three datasets were ascertained in our study, including 135 upregulated genes and 138 downregulated genes. The GO and KEGG enrichment analysis results showed that the functions of DEGs mainly involved in regulation of transcription from RNA polymerase II promoter, specific granule lumen, transcriptional activator activity, Osteoclast differentiation pathway, etc. Through the PPI analysis network, the core genes with the highest degree of 6 nodes were selected: FOS, RHOA, CXCL8, FOSB, EGR1, and DUSP1. Conclusion: The genes, identified in this study, may play a vital regulatory role in the occurrence and development of T1DM. Also, they are closely related to obesity and diabetes mellitus. Our results provide novel biomarkers that could be used as representative reference indicators or potential therapeutic targets for T1DM clinical applications. Presentation: Thursday, June 15, 2023
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".