Recent advances in proximity-based labeling methods for interactome mapping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proximity-based labeling has emerged as a powerful complementary approach to classic affinity purification of multiprotein complexes in the mapping of protein-protein interactions. Ongoing optimization of enzyme tags and delivery methods has improved both temporal and spatial resolution, and the technique has been successfully employed in numerous small-scale (single complex mapping) and large-scale (network mapping) initiatives. When paired with quantitative proteomic approaches, the ability of these assays to provide snapshots of stable and transient interactions over time greatly facilitates the mapping of dynamic interactomes. Furthermore, recent innovations have extended biotin-based proximity labeling techniques such as BioID and APEX beyond classic protein-centric assays (tag a protein to label neighboring proteins) to include RNA-centric (tag an RNA species to label RNA-binding proteins) and DNA-centric (tag a gene locus to label associated protein complexes) assays.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it