Enhanced Interpretable Neural Network Approach for Unified Batch Effect Mitigation and Disease Classification Using Cross-Cohort Microbiome Profiles
Bibliographic record
Abstract
The oral microbiome is a complex environment that consists of diverse microorganisms inhabiting the oral cavity. There are more than 700 different species of bacteria living in the oral cavity which provides nutrition to the microorganisms living in the mouth. As samples tend to be collected with a variation in non-biological factors, batch effects will occur. Batch effects are variations in the same samples, where the variations are affected by the differences in equipment used, the time when the samples were collected, the laboratory conditions, etc. Batch effects can be difficult to address as the variation might not be apparent in individual samples but rather as a whole group between samples. Several research has been proposed to resolve the batch effect, but they tend to require a two-step approach (batch effect removal, and classification), or will suffer from dropout events in gene expressions. In this study, we propose a one-step approach that combines both the batch effect removal and disease classification, eliminating the need for a two-step approach process. LassoNet was used with batch loss to mitigate the effect of batch effect and to classify disease outcome on oral microbiome simultaneously. The model achieved better performance than our baseline models, reaching 0.8 area under the curve on average on the five studies of oral microbiome. In addition, another key aspect of using LassoNet is its ability to carry out feature importance analysis, which is capable to reveal key oral microbiomes associated with disease outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".