Additional file 1 of Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes
Bibliographic record
Abstract
Additional file 1: Fig. S1. Workflow of the study. Fig. S2. Pairwise comparison of disease groups vs the term control, for all omics data. Fig. S3. Pairwise comparison of the FGR+HDP group vs the control-PT group across all omics datatypes. Fig. S4. Shared analytes between the FGR+HDP and the two control groups across all omics data. Fig. S5. Hierarchical clustering for key pairwise comparisons. Fig. S6. RNA canonical pathways and metabolomics enrichment pathway analysis, comparing the FGR+HDP and control groups. Fig. S7. Correlation of expression between clusters II and III, and clusters I and IV. Fig. S8. Deconvolution of cell type in placental bulk RNAseq. Fig. S9. Performance of the elastic net regression in cluster label prediction. Fig. S10. Causal models prediction of SNF cluster labels. Fig. S11. Gene expression in the placenta and maternal plasma. Table S1. Primers for PCR validation. Table S2. Clinical characteristics of the cohort. Table S3. The number of differentially expressed omics analytes across pairwise comparisons. Table S4. Distributions of clinical variables across the SNF clusters. Table S5. Distributions of maternal vascular malperfusion (MVM) lesions across the clinical syndromes and SNF clusters.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.818 | 0.110 |
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".