Relating the molecular phenotype of ulcerative colitis to the clinical course
Bibliographic record
Abstract
The expanding portfolio of targeted therapies for ulcerative colitis (UC) suggests that a more precise approach to defining disease activity will aid clinical decision-making. This prospective study used genome-wide microarrays to characterize gene expression in biopsies from the most inflamed colon segments from patients with UC and analyzed associations between molecular changes and short-term outcomes while on standard-of-care treatment. We analyzed 141 biopsies-128 biopsies from 112 UC patients and 13 biopsies from eight inflammatory bowel disease unclassified (IBDU) patients. Endoscopic disease was associated with expression of innate immunity transcripts, e.g. complement factor B (CFB); inflammasome genes (ZBP1 and PIM2); calprotectin (S100A8 and S100A9); and inflammation-, injury-, and innate immunity-associated pathway analysis terms. A cross-validated molecular machine learning classifier trained on the endoscopic Mayo subscore predicted the endoscopic Mayo subscore with area-under-the-curve of 0.85. A molecular calprotectin transcript score showed strong associations with fecal calprotectin and the endoscopic Mayo subscore. Logistic regression models showed that molecular features (e.g. molecular classifier and molecular calprotectin scores) improved the prediction of disease progression over conventional, clinical features alone (e.g. total Mayo score, fecal calprotectin, physician global assessment). The molecular features of UC showed strong correlations with disease activity and permitted development of machine-learning predictive disease classifiers that can be applied to expanded testing in diverse cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".