Bibliographic record
Abstract
Cell surface proteins, including extracellular matrix proteins, participate in all major cellular processes and functions, such as growth, differentiation, and proliferation. A comprehensive characterization of these proteins provides rich information for biomarker discovery, cell-type identification, and drug-target selection, as well as helping to advance our understanding of cellular biology and physiology. Surface proteins, however, pose significant analytical challenges, because of their inherently low abundance, high hydrophobicity, and heavy post-translational modifications. Taking advantage of the prevalent glycosylation on surface proteins, we introduce here a high-throughput glycopeptide-capture approach that integrates the advantages of several existing N-glycoproteomics means. Our method can enrich the glycopeptides derived from surface proteins and remove their glycans for facile proteomics using LC-MS. The resolved N-glycoproteome comprises the information of protein identity and quantity as well as their sites of glycosylation. This method has been applied to a series of studies in areas including cancer, stem cells, and drug toxicity. The limitation of the method lies in the low abundance of surface membrane proteins, such that a relatively large quantity of samples is required for this analysis compared to studies centered on cytosolic proteins.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".