Analysis of mRNA-lncRNA and mRNA-lncRNA-Pathway co-expression networks based on WGCNA in developing pediatric sepsis
Bibliographic record
Abstract
Abstract Background: Pediatric sepsis is a great threat in death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. Methods: This study identified differentially expressed mRNA (DEMs) and lncRNAs (DELs) based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, DEMs‑DELs and DEMs‑DELs-Pathway co-expression network analysis was conducted in selected significant module. Results: A total of 1941 DEMs and 225 DELs were used to conduct WGCNA. And the turquoise module was selected as the significant module that was associated with particular traits. The DEMs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 DEMs and 4 DELs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as candidate biomarkers in DEMs-DELs-Pathway co-expression network. Conclusions: Our study identified 15 DEMs and 4 DELs as diagnostic markers, which could also provide more directions to study molecular mechanism of pediatric sepsis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".