Secretome profiling with antibody microarrays
Bibliographic record
Abstract
Following the advances in human genome sequencing, attention has shifted in part toward the elucidation of the encoded biological functions. Since proteins are the driving forces behind very many biological activities, large-scale examinations of their expression variations, their functional roles and regulation have moved to the central stage. A significant fraction of the human proteome consists of secreted proteins. Exploring this set of molecules offers unique opportunities for understanding molecular interactions between cells and fosters biomarker discovery that could advance the detection and monitoring of diseases. Antibody microarrays are among the relatively new proteomic methodologies that may advance the field significantly because of their relative simplicity, robust performance and high sensitivity down to single-molecule detection. In addition, several aspects such as variations in amount, structure and activity can be assayed at a time. Antibody microarrays are therefore likely to improve the analytical capabilities in proteomics and consequently permit the production of even more informative and reliable data. This review looks at recent applications of this novel platform technology in secretome analysis and reflects on the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".