DR-BERT
Efficiently annotate disordered protein regions with a compact language model.
DR-BERT is a compact protein language model designed to annotate intrinsically disordered regions (IDRs) in proteins. Unlike traditional tools that rely on evolutionary or biophysical features, DR-BERT leverages pretraining on large-scale protein datasets to learn contextual information. This innovative approach achieves statistically significant improvements in predicting disordered regions, offering researchers a high-accuracy, lightweight alternative for protein annotation.
Estimated resource cost: 0.0002945
Categories: Sequence Analysis & Annotation
Tags: Proteins
Key Capabilities
- Pretrained on extensive unannotated protein datasets for enhanced contextual learning.
- Does not rely on evolutionary or biophysical features, simplifying input requirements.
- Outperforms several existing methods on gold standard datasets with statistically significant accuracy.
- Compact and efficient, suitable for high-throughput protein annotation tasks.
- Web application available for easy access and real-time annotation tasks.
Runtime Statistics
| Metric | Value |
|---|---|
| runtime_mean | 6 |
| runtime_median | 6 |
| runtime_std | 1 |
| runtime_90th_percentile | 7 |
| runtime_max | 18 |
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Estimated Credits: 0.0002945
Invite-only, limited-time access. Please contact ztang@getantibody.com.
Invite-only, limited-time access. Please contact ztang@getantibody.com.