AtLASS: A Scheme for End-to-End Prediction of Splice Sites Using Attention-based Bi-LSTM
Bibliographic record
Abstract
Eukaryotic genomes contain exons and introns, and it is necessary to accurately identify exon-intron boundaries, i.e., splice sites, to annotate genomes. To address this problem, many previous works have proposed annotation methods/tools based on RNA-seq evidence. Many recent works exploit neural networks (NNs) as their prediction models, but only a few can be used to generate new genome annotation in practice. In this study, we propose AtLASS, a fully automated method for predicting splice sites from genomic and RNA-seq data using attention-based Bi-LSTM (Bidirectional Long Short-Term Memory). We exploit two-stage training on RNA-seq data to address the problem of biased label problem, thereby reducing the false positives. The experiments on the genomes of three species show that the performance of the proposed method itself is comparable to that of existing methods, but we can achieve better performance by combining the outputs of the proposed method and the existing method. The proposed method is the first program specialized in end-to-end splice site prediction using NNs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".