PaRPI predicts RNA-Protein interactions from cross-protocol and cross-batch RNA-binding protein datasets
Bibliographic record
Abstract
RNA-binding proteins (RBPs) play a pivotal role in the regulation of gene expression, with their interactions with RNA reflecting the biological functions and regulatory mechanisms. However, current computational methods are typically tailored to specific RBPs and depend on specific protocols and batches of biological experiments. To overcome these challenges, we propose a method called PaRPI, which aims to predict RNA-protein binding sites in a bidirectional RBP-RNA selection manner. PaRPI groups all RBP datasets based on cell lines, integrating experimental data from different protocols and batches, thereby enabling the development of a unified computational model that effectively captures both shared and distinct interaction patterns among different proteins. Our results demonstrate that PaRPI achieves exceptional performance in accurately identifying binding sites, surpassing state-of-the-art models on 261 RBP datasets from eCLIP and CLIP-seq experiments. Furthermore, PaRPI stands out for its robust generalization capabilities, uniquely able to predict interactions with previously unseen RNA and protein receptors. We also investigate the impact of disease-associated variants on RBP binding and evaluate PaRPI's components and semantic embeddings, demonstrating its capability to dissect complex interaction networks. PaRPI enables large-scale exploration of RNA-protein interactions, facilitating future studies on gene regulation and disease mechanisms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".