Exacerbation risk prediction in pediatric wheeze or asthma patients using machine-learning-based integrative omics analyses.
Bibliographic record
Abstract
Introduction: Omics data has augmented the molecular understanding of phenotypes in asthma; however, it is unclear if integrating omics with clinical data can improve the accurate prediction of future exacerbation risks in asthma. Objective: To assess if integrating gene expression and nasopharyngeal microbiome data to clinical data improves the prediction of future exacerbation risk in children with wheeze or asthma. Methods: In the U-BIOPRED pediatric prospective cohort study, we integrated omics and clinical data from children with wheeze or asthma. We included preschoolers aged 1-5 years (n=103) and children aged 6-17 years (n=111) with mild-to-moderate wheeze (n=40), severe wheeze (n=63), mild-to-moderate asthma (n=37) or severe asthma (n=74) with clinical, gene expression, and microbiome data. Exacerbation was defined as an oral corticosteroid burst, urgent visit, or hospitalization for asthma during the 12-18 months after enrolment. Multiple imputation was used to address missing data for exacerbations and clinical variables. We compared the area under the curve (AUC) metrics for predicting exacerbation with vs. without omics data using Logistic Regression (LR), XGBoost (XGB), and Feedforward Neural Network (FFNN) models. Results: LR (AUC 0.82), XGB (AUC 0.79), and FFNN (AUC 0.81) achieved similar performance using clinical data only. Incorporating gene expression and microbiome data significantly boosted FFNN (AUC 0.94) and XGB (AUC 0.86) performance but not LR (AUC 0.81). Conclusion: Omics integration using gene expression and nasopharyngeal microbiome data added predictive value for future exacerbation risk in children with wheeze and/or asthma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".