Stool-Based Proteomic Signature for the Noninvasive Classification of Crohn's Disease and Ulcerative Colitis Using Machine Learning
Bibliographic record
Abstract
INTRODUCTION: Crohn's disease (CD) and ulcerative colitis (UC) have overlapping symptoms, but they differ in pathology and treatment. Currently, distinguishing between these diseases involves invasive procedures such as colonoscopy and histopathology. Fecal proteins, stable and in direct contact with inflammation, offer a noninvasive alternative. This study focuses on using high-throughput data-independent acquisition mass spectrometry and machine learning to develop an accurate biomarker signature from complex stool samples. METHODS: Stool samples obtained from 69 active patients were analyzed. Analysis of the stool proteome led to the identification and quantification of approximately 1,250 proteins. The samples were divided into training and testing groups. After data processing, various feature selection algorithms were applied on the training group to determine proteins that were significantly different between the CD and UC groups. In addition, 6 machine learning algorithms were evaluated to identify the best-performing classifiers. RESULTS: Sixteen proteins were selected based on several feature selection algorithms, and 6 models were trained based on them. According to the performance metrics of each algorithm on the training data set, the Naive Bayes model was selected. For performance validation, the final predictive model was applied to 16 blind prospective samples as the test data set. Notably, the model achieved an area under the curve of 0.96 on both the training and test data sets, highlighting its robustness and stability. DISCUSSION: This study demonstrates the potential of combining multiple stool protein biomarkers through high-throughput data-independent acquisition mass spectrometry and machine learning tools to develop a predictive model for efficiently distinguishing CD from UC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".