Bibliographic record
Abstract
Measurement of biologically important effector protein molecules has been a long-standing essential component of biological research. Advances in biotechnology, in the form of high-resolution mass spectrometers, and in bioinformatics, now allow the simultaneous quantitative analysis of thousands of proteins. While these techniques still do not allow definitive identification of the entire proteome of complex mixtures, such as cells, quantitative analyses of hundreds to thousands of proteins in such complex mixtures provides a means to elucidate molecular alterations that occur during perturbation of cellular systems. This article will outline considerations of reducing sample complexity, by strategies such as multidimensional separations (gel-based and chromatography-based, including multidimensional protein identification technology). In addition, some of the most common methods used to quantitatively measure proteins in complex mixtures (2D difference in-gel electrophoresis, isotope-coded affinity tags, isotope-coded protein labeling, tandem mass tags, isobaric tags for relative and absolute quantitation, stable isotope labeling of amino acids in cell culture and label-free), as well as recent examples of each strategy, are described.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".