Clinical Microbiome Analysis by Mass Spectrometry–Based Metaproteomics
Bibliographic record
Abstract
Mass spectrometry-based proteomics and metaproteomics have long been used in the study of human microbiomes, with the potential of metaproteomics only recently being fully harnessed. This progress is due to the advancements of high-performance mass spectrometers, innovative proteomics strategies, and the development of dedicated bioinformatics tools. In this review, we critically examine the recent technological developments that enhance the application of metaproteomics in clinical microbiome analysis. We also summarize significant advancements in the application of metaproteomics to study human microbiomes across various body sites under disease conditions. Despite these, the potential of metaproteomics remains underutilized due to typically small sample sizes and insufficient data mining. We thereby highlight some key aspects that could facilitate the broader and more effective application of mass spectrometry-based metaproteomics in clinical microbiome analysis, including the development of microbiome assays for translational research and application.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".