RBP Image Database: A resource for the systematic characterization of the subcellular distribution properties of human RNA binding proteins
Bibliographic record
Abstract
RNA binding proteins (RBPs) are central regulators of gene expression implicated in all facets of RNA metabolism. As such, they play key roles in cellular physiology and disease etiology. Since different steps of post-transcriptional gene expression tend to occur in specific regions of the cell, including nuclear or cytoplasmic locations, defining the subcellular distribution properties of RBPs is an important step in assessing their potential functions. Here, we present the RBP Image Database, a resource that details the subcellular localization features of 301 RBPs in the human HepG2 and HeLa cell lines, based on the results of systematic immuno-fluorescence studies conducted using a highly validated collection of RBP antibodies and a panel of 12 markers for specific organelles and subcellular structures. The unique features of the RBP Image Database include: (i) hosting of comprehensive representative images for each RBP-marker pair, with ∼250,000 microscopy images; (ii) a manually curated controlled vocabulary of annotation terms detailing the localization features of each factor; and (iii) a user-friendly interface allowing the rapid querying of the data by target or annotation. The RBP Image Database is freely available at https://rnabiology.ircm.qc.ca/RBPImage/.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.050 |
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".