Development and validation of asthma risk prediction models using co- expression gene modules and machine learning methods
Bibliographic record
Abstract
Abstract Asthma is a chronic inflammatory disease of the airways with a strong genetic component. Because multiple genes may affect asthma, identifying differentially co-expressed genes followed by functional annotation can inform our understanding of the molecular mechanisms in asthma pathogenesis. In this study, we used airway epithelial cells (AECs) and nasal epithelial cells (NECs) datasets and implemented weighted gene co-expression network analysis (WGCNA) and machine learning (ML) techniques to develop asthma classification and predictive models. The models were validated using external bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. WGCNA and ML-based procedure identified 23 and 34 gene signatures that can discriminate asthmatic from control subjects in AECs (Area under the curve: AUC =0.90) and NECs (AUC = 0.99), respectively. We further validated AECs derived DEGs in BECs (AUC= 0.96), ASM (AUC= 0.72) and WB (AUC= 0.67). Similarly, NECs derived DEGs in BECs (AUC= 0.88), ASM (AUC= 0.87) and WB (AUC= 0.68). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of NEC-derived hub genes showed several enriched pathways related to Th1 and Th2 activation pathway, while AECs-derived hub genes were significantly enriched in pulmonary fibrosis and idiopathic signaling. Several asthma related genes were prioritized including Cathepsin C (CTSC) which showed functional relevance in multiple cells relevant to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve asthma classification.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".