Metadata-Version: 2.4
Name: tempo-ml
Version: 1.1.0
Summary: TEMPO: A Hybrid-Neuromorphic Pipeline for Efficient Multi-Omic High-Dimensional Oncology Data Integration
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: snntorch>=0.7.0
Requires-Dist: pandas>=1.5.0
Requires-Dist: numpy>=1.23.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: scipy>=1.9.0
Requires-Dist: matplotlib>=3.6.0
Requires-Dist: seaborn>=0.12.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Dynamic: license-file

# TEMPO: A Hybrid-Neuromorphic Pipeline for Efficient Multi-Omic Cancer Data Integration

TEMPO is a professional-grade, energy-efficient Python package designed for multi-omic cancer data integraton. Using an innovative hybrid ANN-SNN hybrid model and advanced features, TEMPO has drastically reduces computational overhead (Synaptic Operations and FLOPs) while maintaining state-of-the-art classification performance on highly imbalanced pan-cancer datasets.

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## Key Features

* **Multi-Omic Integration:** Seamlessly aligns disparate modalities (e.g., mRNA expression, Proteomics, CNV, Clinical data).
* **Neuromorphic Efficiency:** Leverages Leaky Integrate-and-Fire (LIF) neurons to minimize computational power.
* **Smart Routing & Gating:** Implements Gumbel-Softmax modality routing and Fast Fourier Transform (FFT) semantic addressing.
* **Scikit-Learn Style API:** Clean, intuitive `.fit()` and `.predict()` wrapper built over PyTorch execution blocks.
* **Imbalance-Aware:** Built-in balanced Focal Loss and automated sample weighting to handle rare cancer subtypes.

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## Installation

To install TEMPO locally in editable development mode, navigate to your root package directory and run:

```bash
pip install -e .
