Proteomics‐based investigations of animal models of disease
Bibliographic record
Abstract
Cells contain a large yet, constant genome, which contains all the coding information necessary to sustain cellular physiology. However, proteins are the end products of genes, and hence dictate the phenotype of cells and tissues. Therefore, proteomics can provide key information for the elucidation of physiological and pathophysiological mechanisms by identifying the protein profile from cells and tissues. The relatively novel techniques used for the study of proteomics thus have the potential to improve diagnostic, prognostic, as well as therapeutic avenues. In this review, we first discuss the benefits of animal models over the use of human samples for the proteomic analysis of human disease. Next, we aim to demonstrate the potential of proteomics in the elucidation of disease mechanisms that may not be possible by other conventional technologies. Following this, we describe the use of proteomics for the analysis of PTM and protein interactions in animal models and their relevance to the study of human disease. Finally, we discuss the development of clinical biomarkers for the early diagnosis of disease via proteomic analysis of animal models. We also discuss the development of standard proteomes and relate how this data will benefit future proteomic research. A comprehensive review of all animal models used in conjunction with proteomics is beyond the scope of this manuscript. Therefore, we aimed to cover a large breadth of topics, which together, demonstrate the potential of proteomics as a powerful tool in biomedical research.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".