Skip to main content

TEMPO: A Hybrid-Neuromorphic Pipeline for Efficient Multi-Omic High-Dimensional Oncology Data Integration

Project description

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.


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.

Installation

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

pip install -e .

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tempo_ml-1.1.0.tar.gz (7.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tempo_ml-1.1.0-py3-none-any.whl (7.6 kB view details)

Uploaded Python 3

File details

Details for the file tempo_ml-1.1.0.tar.gz.

File metadata

  • Download URL: tempo_ml-1.1.0.tar.gz
  • Upload date:
  • Size: 7.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for tempo_ml-1.1.0.tar.gz
Algorithm Hash digest
SHA256 11d40b6157745e2dacd664a160bc9d3397fb539223d53a563d55cf463d742341
MD5 9218424457886c16af4bfe19c0c207fb
BLAKE2b-256 a14b99a87c237fd56e9ba68b69e20cea4bac313fdb81c1eb60c8f5772d1be786

See more details on using hashes here.

Provenance

The following attestation bundles were made for tempo_ml-1.1.0.tar.gz:

Publisher: publish.yml on Lucia-N/TEMPO

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file tempo_ml-1.1.0-py3-none-any.whl.

File metadata

  • Download URL: tempo_ml-1.1.0-py3-none-any.whl
  • Upload date:
  • Size: 7.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for tempo_ml-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 b299ad4006ab22d7bbca7caeefa68999000487aa9790767d3a1d97927c39cb58
MD5 6e53bc134d1b234ccb30606c4ba51e71
BLAKE2b-256 df309f322a06d6717b1bdd955e82aec8d491b36087920f7f87f47f4e318352a1

See more details on using hashes here.

Provenance

The following attestation bundles were made for tempo_ml-1.1.0-py3-none-any.whl:

Publisher: publish.yml on Lucia-N/TEMPO

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page