Mass-Coded Abundance Tagging for Protein Identification and Relative Abundance Determination in Proteomic Experiments
Bibliographic record
Abstract
Advances in mass spectrometry have led to the emergence of the distinct field of proteomics. One aim of proteomics, the identification of the protein components of complex biological mixtures, is now routinely realized, typically by peptide mass mapping following matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) or by peptide sequence determination from tandem mass spectra obtained by electrospray ionization followed by collision-induced dissociation (CID) ( 1 ). Both approaches rely on the identified proteins being present in DNA or protein sequence databases. This is because the behavior of ionized peptides in MS experiments is somewhat unpredictable and the resulting spectra are searched against “idealized” spectra generated from the sequence databases to find the nearest match. Nevertheless, both approaches have been highly successful, with thousands of proteins identified in a single large-scale analysis (reviewed in ref. 2 ). A method that is independent of databases would be useful in certain cases, however, especially for protein samples deriving from organisms whose genomes remain unsequenced, proteins with erroneous sequences deposited in the databases, or proteins whose splicing patterns or modification states are unknown. Another partially fulfilled goal of proteomics is to determine the quantities of each protein present in a mixture, or at least the relative abundance of proteins present in two different samples, such as a test sample and a reference control. Several approaches for determining relative abundance in proteomic experiments have involved differential incorporation of stable isotopes into one of the samples, using either labeled growth media ( 3 ) or postexperimental chemical labeling ( 4 , 5 ). At least two methods using nonisotopic reagents for purposes of proteomic quantification have recently been reported ( 6 , 7 ) These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".