Enhancement of mass spectrometry performance for proteomic analyses using high‐field asymmetric waveform ion mobility spectrometry (FAIMS)
Bibliographic record
Abstract
Remarkable advances in mass spectrometry sensitivity and resolution have been accomplished over the past two decades to enhance the depth and coverage of proteome analyses. As these technological developments expanded the detection capability of mass spectrometers, they also revealed an increasing complexity of low abundance peptides, solvent clusters and sample contaminants that can confound protein identification. Separation techniques that are complementary and can be used in combination with liquid chromatography are often sought to improve mass spectrometry sensitivity for proteomics applications. In this context, high-field asymmetric waveform ion mobility spectrometry (FAIMS), a form of ion mobility that exploits ion separation at low and high electric fields, has shown significant advantages by focusing and separating multiply charged peptide ions from singly charged interferences. This paper examines the analytical benefits of FAIMS in proteomics to separate co-eluting peptide isomers and to enhance peptide detection and quantitative measurements of protein digests via native peptides (label-free) or isotopically labeled peptides from metabolic labeling or chemical tagging experiments.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".