Proteomics in Veterinary Medicine
Bibliographic record
Abstract
Advancement in electrophoresis and mass spectrometry techniques along with the recent progresses in genomics, culminating in bovine and pig genome sequencing, widened the potential application of proteomics in the field of veterinary medicine. The aim of the present review is to provide an in-depth perspective about the application of proteomics to animal disease pathogenesis, as well as its utilization in veterinary diagnostics. After an overview on the various proteomic techniques that are currently applied to veterinary sciences, the article focuses on proteomic approaches to animal disease pathogenesis. Included as well are recent achievements in immunoproteomics (ie, the identifications through proteomic techniques of antigen involved in immune response) and histoproteomics (ie, the application of proteomics in tissue processed for immunohistochemistry). Finally, the article focuses on clinical proteomics (ie, the application of proteomics to the identification of new biomarkers of animal diseases).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".