Bibliographic record
Abstract
Research studies have shown that human microbiome is associated with many diseases through the linkage between bacterial taxa and environmental and genetic factors. Typical human microbiome sequencing data that obtained by next generation sequencing technologies of the 16S rRNA gene are high dimensional and sparse because most taxa are not shared among the samples. As a result, the data is often over-dispersed and with excess zeros. These features rise statistical challenges for compositional data analysis. We review the recent statistical methodology development for this setting. In particular, we summarize some current popular parametric probability models including the cases when repeated measurements of the microbiome are applicable. Multivariate analyses methods that are based on distance measurement for testing differences between microbes community are introduced. Statistical models which are developed to assess the association between genetic variants on X-chromosome and microbial components are highlighted. We discuss some applications on analysis of the association of host genome, microbial compositions and human diseases. Despite sophisticated approaches to statistical analysis of taxa count data, we suggest some future research directions on how to classify and predict clinical outcomes with microbial compositions.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".