Large-Scale Characterization of the Murine Cardiac Proteome
Bibliographic record
Abstract
Cardiomyopathies are diseases of the heart that result in impaired cardiac muscle function. This dysfunction can progress to an inability to supply blood to the body. Cardiovascular diseases play a large role in overall global morbidity. Investigating the protein changes in the heart during disease can uncover pathophysiological mechanisms and potential therapeutic targets. Establishing a global protein expression "footprint" can facilitate more targeted studies of diseases of the heart.In the technical review presented here, we present methods to elucidate the heart's proteome through subfractionation of the cellular compartments to reduce sample complexity and improve detection of lower abundant proteins during multidimensional protein identification technology analysis. Analysis of the cytosolic, microsomal, and mitochondrial subproteomes separately in order to characterize the murine cardiac proteome is advantageous by simplifying complex cardiac protein mixtures. In combination with bioinformatic analysis and genome correlation, large-scale protein changes can be identified at the cellular compartment level in this animal model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".