Mixtures of logistic normal multinomial regression models for microbiome data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the realm of bioinformatics, we frequently encounter discrete data, particularly microbiome taxa count data obtained through 16S rRNA sequencing. These microbiome datasets are commonly characterized by their high dimensionality and the ability to provide insights solely into relative abundance, necessitating their classification as compositional data. Analyzing such data presents challenges due to their confinement within a simplex. Additionally, microbiome taxa counts are subject to influence by various biological and environmental factors like age, gender, and diet. Thus, we have developed a novel approach involving regression-based mixtures of logistic normal multinomial models for clustering microbiome data. These models effectively categorize samples into more homogeneous subpopulations, enabling the exploration of relationships between bacterial abundance and biological or environmental covariates within each identified group. To enhance the accuracy and efficiency of parameter estimation, we employ a robust framework based on variational Gaussian approximations (VGA). Our proposed method's effectiveness is demonstrated through its application to simulated and real datasets.
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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 | 0.000 | 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 it