Bibliographic record
Abstract
Compartmentalization is essential for all complex forms of life. In eukaryotic cells, membrane-bound organelles, as well as a multitude of protein- and nucleic acid-rich subcellular structures, maintain boundaries and serve as enrichment zones to promote and regulate protein function, including signaling events. Understanding the composition of each cellular “compartment” (be it a classical organelle or a large protein complex) remains a challenging task. For soluble protein complexes, different approaches coupled to mass spectrometry such as affinity purification, biochemical fractionation and proximity labeling of proteins in vivo using biotin-transfer provide important insight. For detergent-insoluble components, proximity labeling has been very effective in identifying protein neighbors, leading to rapid uptake of this technique in the scientific community. This presentation will introduce and compare protein-protein interaction mapping techniques and describe how to design, execute, and analyze proximity labeling experiments, with an emphasis on performing these experiments in a core facility. We have generated a human cell map of major organelles and non-membrane-bound structures from proteins profiled by in vivo biotinylation (BioID). This resource is now a community tool, and I will discuss how it can be used to better interpret BioID results. Since its introduction less than a decade ago, there has been tremendous progress in applying and improving proximity labeling techniques, and I will conclude with a glimpse of what is next on the horizon for proximity mapping.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".