No project description provided
Project description
Morpheus: Generating Therapeutic Strategies using Spatial Omics
Introduction
Morpheus is an integrated deep learning framework that takes large scale spatial omics profiles of patient tumors, and combines a formulation of T-cell infiltration prediction as a self-supervised machine learning problem with a counterfactual optimization strategy to generate minimal tumor perturbations predicted to boost T-cell infiltration.
Features
- Self-Supervised Learning: Utilizes unlabeled spatial omics data to learn predictive models for T-cell infiltration.
- Counterfactual Reasoning: Generates minimal perturbations to the tumor environment, hypothesizing potential improvements in T-cell responses.
- Deep Learning Integration: Employs advanced neural network architectures tailored for high-dimensional omics data.
- Scalability: Designed to handle large datasets typical of spatial omics studies, enabling robust analysis across numerous patient samples.
Getting Started
Prerequisites
- Python 3.9 or higher
- PyTorch Lightning 2.2.0 or higher
- CUDA 11.7 or higher (for GPU acceleration)
- Other dependencies listed in
requirements.txt
Installation
Run the following in the command line
pip install morpheus-spatial
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
morpheus_spatial-0.2.0.tar.gz
(54.2 kB
view hashes)
Built Distribution
Close
Hashes for morpheus_spatial-0.2.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | df064f8eb282e1ec9dd349d767328649fa4a36ba33d5b9fbfa1b0a905fcbd234 |
|
MD5 | afdcc8cd77f647571c8455bdf6f7d794 |
|
BLAKE2b-256 | ba5f6f3de03f20547fdf85839cd6e1179f6a35bdc92063a400ae38978b70fc45 |