Machine learning approaches for the prediction of binding sites for RNA binding proteins
Bibliographic record
Abstract
RNA binding proteins (RBPs) play an essential role in many biological processes.Understanding the specific binding preferences of RBPs helps us in understanding the various steps of gene expression and may help in solving several genetic disorders.There are thousands of RBPs in humans, and only a small fraction of them are well understood.Current experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from high false negative rate.In this work, we develop deep neural network models that allow us to learn binding preferences for a large number of RBPs from CLIP-seq data.We developed three deep architectures and used them to predict RNA-protein binding.We further analyze the importance of RNA secondary structure in RBP binding by incorporating computationally predicted secondary structure features as input to our models.We evaluate our model on the publicly available dataset of RBP binding sites derived from CLIP-seq.The results demonstrate that our approach achieves better or comparable performance with other state-of-the-art methods.Further, our model is able to automatically capture the interpretable binding motifs for several RBPs.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".