Constructing Long Short-Term Memory Networks to Predict Ulcerative Colitis Progression from Longitudinal Gut Microbiome Profiles
Bibliographic record
Abstract
Introduction & Objective: Ulcerative colitis is an intestinal disorder with an erratic progression in which the patients suffer from capricious remissions and changeful severities. Lacking prognosis to the UC progression can lead to irrational treatments that adversely affect the patients’ quality of life. Existing studies have stated a connection between gut microbiomes and UC progression. We aim to construct Long Short-Term Memory (LSTM) networks to predict UC progression (remission & severity) from longitudinal gut microbiome data. Methods: Using one-step and two-step modelling strategies, we develop a standard LSTM network, an encoder-decoder LSTM network, a convolutional LSTM network, and several benchmarking classifiers such as random forests. For high-dimensional data, we also implement auto-encoder to select variables in addition to baseline procedures like principal component analysis. We train each model using a longitudinal microbiome data, and validate them via a 10-round set splitting approach. Results: Each proposed model shows the potential to predict UC progression, but they do not reach an optimal level for medical utilizations. The encoder-decoder LSTM demonstrates superiority over the other classifiers while the auto-encoder outperformed the baseline variable selectors. Conclusion: We support the capacity of Long Short-Term Memory (LSTM) networks to predict UC progression from longitudinal microbiome data, and verify the strength of autoencoder networks in selecting features from high dimensional data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".