Recent Progress of Deep Learning Methods for RBP Binding Sites Prediction on circRNA
Bibliographic record
Abstract
The interaction between circular RNA (circRNA) and RNA binding protein (RBP) plays an important biological role in the occurrence and development of various diseases. Highthroughput biological experimental methods such as CLIP-seq can effectively analyze the interaction between the two, but biological experiments are inefficient and expensive, and they can only capture binding sites of a specific RBP on circRNA in a selected cell environment at a time. These biological experiments still rely on downstream data analysis to understand the mechanisms behind many biological structures and physiological processes. However, the rapid growth of experimental data dimensions and production speed pose challenges to traditional analysis methods. In recent years, deep learning has made great progress in the genome and transcriptome, and some deep learning prediction algorithms for RBP binding sites on circRNA have also emerged. In this paper, we briefly introduce some biological background knowledge related to circRNA-RBP interaction; present relevant deep learning techniques in this field, including the problem formulation, data source, sequence encoding, deep learning model and overall process of RBP binding sites prediction on circRNA; deeply analyze the current deep learning methods. Finally, some problems existing in the current research and the direction of future research are discussed. It is hoped to help researchers without basic knowledge of deep learning or basic biological background quickly understand the RBP binding sites prediction on circRNA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".