Additional file 2 of RapidAIM: a culture- and metaproteomics-based Rapid Assay of Individual Microbiome responses to drugs
Bibliographic record
Abstract
Additional file 2: Figure S1. Establishment and step-by-step validation of the microplate-based metaproteomic sample preparation workflow of the RapidAIM assay. Figure S2. Assessment of the equal-volume digestion and LC-MS/MS analysis strategy. Figure S3. Data quality check of the POC dataset. Figure S4. Reproducibility of RapidAIM assay on different levels. Figure S5. Case study on microbiome V1’s response to rifaximin. Figure S6. Log2 fold-change of relative abundance at the genus level in response to each drug compared with the DMSO control. Figure S7. Score plots and cross-validations of seven PLS-DA models. Figure S8. Log2 fold-change of functions at the COG protein level. Figure S9. String interaction of COG functional proteins significantly stimulated by diclofenac. Figure S10. Response of enzymes along the butyrate production from Acetyl-CoA. Figure S11. Phylum-specific functional responses to Berberine. Figure S12. Randomly selected LFQ intensities of protein groups showing heavy tailed distribution on the Q-Q plots. Figure S13. Randomly selected log2-fold changes of COGs showing heavy tailed distribution on the Q-Q plots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.832 | 0.213 |
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".