PROTEOMICS
Bibliographic record
Abstract
PROTEOMICS, IN ITS broadest mandate, is the study of proteins and their functions. As the "workhorses" of the genome, proteins govern normal cellular structure and function. Protein function is not just a reflection of its expression level; it is also the cumulative result of many post-transcriptional (splicing) and post-translational events that together determine cellular localization, interactions, and longevity. The composition and variability of the proteome is vastly more complex than the corresponding genome. It is this proteome variation that helps define an organism and the unique characteristics that separate one individual from another. Aberrations in protein function, which alter normal cellular structure and function, are the ultimate basis of disease, including cancer. Therefore, an understanding of protein networks through a systems biology approach of proteomics is necessary to understand normal and abnormal cellular function, with the goal of performing rational therapeutic interventions. In this review, we focus on two emerging proteomic technologies: mass spectrometry and bioluminescence resonance energy transfer. In addition to reviewing the principles and potential utilization of these two techniques, we highlight their application in neuro-oncology 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.126 |
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".