CR
aertslab/CREsted
CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data, for capturing enhancer code and for designing cell type-specific sequences.
62 9 +0/wk
GitHub
deep-learning regulatory-genomics scatac-seq scverse sequence-models single-cell-genomics
Trend
3
Star & Fork Trend (15 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
aertslab/CREsted has +0 stars this period . 7-day velocity: 1.6%.
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Signal-backed technical analysis will be available soon.
| Metric | CREsted | EEGDash | born | CourtKeyNet |
|---|---|---|---|---|
| Stars | 62 | 62 | 62 | 62 |
| Forks | 9 | 9 | 7 | 0 |
| Weekly Growth | +0 | +0 | +2 | +0 |
| Language | Python | Python | Go | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | NOASSERTION | BSD-3-Clause | Apache-2.0 | MIT |
Capability Radar vs EEGDash
CREsted
EEGDash
Maintenance Activity 100
Last code push 2 days ago.
Community Engagement 73
Fork-to-star ratio: 14.5%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 30
No clear license detected — proceed with caution.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.