# Core scientific stack
numpy>=1.26
pandas>=1.5
scipy>=1.10
scikit-learn>=1.3
scikit-image>=0.23
numba>=0.57

# Single-cell ecosystem
anndata>=0.10
scanpy>=1.10
muon>=0.1.6
mousipy>=0.1.0

# Deep learning and model training
torch>=2.0
pytorch-tabnet>=4.0
optuna>=3.4
imbalanced-learn>=0.11

# Explainability and visualization
shap>=0.46,<0.50
matplotlib>=3.6
seaborn>=0.12
plottable>=0.1.4

# Utilities and I/O
tqdm>=4.36
packaging>=21.3
fsspec>=2024.2.0
aiohttp>=3.8
requests>=2.28