StrucTFactor
StrucTFactor leverages 3D protein structures for precise transcription factor prediction, outperforming existing methods.
StrucTFactor is a novel deep learning-based method for predicting transcription factors by utilizing 3D secondary structural features of proteins. Unlike traditional sequence-based methods, StrucTFactor employs a CNN-based architecture to analyze structural information, significantly improving the accuracy of transcription factor prediction. The model has been evaluated on extensive datasets, outperforming state-of-the-art methods in metrics like Matthews correlation coefficient and AU-PRC. It is an innovative tool for identifying novel transcription factors and offers insights into DNA-binding domains without requiring prior annotations.
Estimated resource cost: 0.000248
Categories: Sequence Analysis & Annotation
Tags: Proteins
Key Capabilities
- Predicts transcription factors using 3D secondary structural features of proteins.
- Outperforms state-of-the-art methods like DeepTFactor and DeepReg in accuracy and robustness.
- Leverages secondary structural annotations (α-helix, β-sheet, coil-turn).
- Applicable for identifying novel transcription factors without requiring prior knowledge of DNA-binding domains.
Runtime Statistics
| Metric | Value |
|---|---|
| runtime_mean | 300 |
| runtime_median | 10 |
| runtime_std | 1322 |
| runtime_90th_percentile | 16 |
| runtime_max | 6362 |
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Estimated Credits: 0.000248
Invite-only, limited-time access. Please contact ztang@getantibody.com.
Invite-only, limited-time access. Please contact ztang@getantibody.com.