Unsupervised Placental Gene Expression Profiling Identifies Clinically Relevant Subclasses of Human Preeclampsia
Bibliographic record
Abstract
Preeclampsia (PE) is a complex, hypertensive disorder of pregnancy, demonstrating considerable variability in maternal symptoms and fetal outcomes. Unfortunately, prior research has not accounted for this variability, resulting in a lack of robust biomarkers and effective treatments for PE. Here, we created a large (N=330) clinically relevant human placental microarray data set, consisting of 7 previously published studies and 157 highly annotated new samples from a single BioBank. Applying unsupervised clustering to this combined data set identified 3 clinically significant probable etiologies of PE: "maternal", with healthy placentas and term deliveries; "canonical", exhibiting expected clinical, ontological, and histopathologic features of PE; and "immunologic" with severe fetal growth restriction and evidence of maternal antifetal rejection. Moreover, these groups could be distinguished using a small quantitative polymerase chain reaction panel and demonstrated varying influence of maternal factors on PE development. An additional subclass of PE placentas was also revealed to form because of chromosomal abnormalities in these samples, supported by array-based comparative genomic hybridization analysis. Overall, our findings represent a new paradigm in our understanding of the origins and maternal-placental contributions to the pathology of PE. The study of PE represents a unique opportunity to access human tissue associated with a complex hypertensive disorder, and our novel approach could be applied to other hypertensive and heterogeneous human diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".