Gene ontology enrichment analysis of congenital diaphragmatic hernia-associated genes
Bibliographic record
Abstract
Congenital diaphragmatic hernia (CDH) is a commonly occurring major congenital anomaly with a profound impact on neonatal mortality. The etiology of CDH is poorly understood and is complicated by multiple clinical presentations, reflecting the location and type of diaphragm defect. With the increased power of genetic screening, more genes are being associated with CDH, creating a knowledge gap between CDH-associated genes and their contribution to diaphragm embryogenesis. Our goal was to investigate CDH-associated genes and identify common pathways that may lead to abnormal diaphragm development. A comprehensive list of CDH-associated genes was identified from the literature and categorized according to multiple factors, including type of CDH. We undertook a large-scale gene function analysis using gene ontology to identify significantly enriched biological pathways and molecular functions associated with our gene set. We identified 218 CDH-associated genes. Our gene ontology analysis showed that genes representing distinct biological pathways are significantly enriched in relation to different clinical presentations of CDH. This includes retinoic acid signaling in Bochdalek CDH, myogenesis in diaphragm eventration, and angiogenesis in central tendon defects. We have identified unique genotype-phenotype relationships highlighting the major genetic drivers of the different types of CDH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".