Skip to main content

NaroNet: discovery of tumor microenvironment elements from multiplex images.

Project description

NaroNet: discovery of tumor microenvironment elements from multiplex imaging.

Trained only with patient-level labels, NaroNet quantifies the phenotypes, neighborhoods, and neighborhood interactions that have the highest influence on the predictive task. This is the python implementation as described in our paper.

Installation

For GPU support, it is crucial to install the specific versions of CUDA that are compatible with the version of Pytorch. For further information follow our github page

To install NaroNet:

pip install NaroNet

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

NaroNet-1.0.3.tar.gz (122.7 kB view details)

Uploaded Source

File details

Details for the file NaroNet-1.0.3.tar.gz.

File metadata

  • Download URL: NaroNet-1.0.3.tar.gz
  • Upload date:
  • Size: 122.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.12

File hashes

Hashes for NaroNet-1.0.3.tar.gz
Algorithm Hash digest
SHA256 14beb66367ef07d6e7e947f7b81060d20dc60b5c758f1bbc05e6bd3b3e7a3dd2
MD5 aa61d38c3cdb66b86bfedc96e416d959
BLAKE2b-256 56d3f4b8304876a31fec30d7c99cff41f175b0bdfb3e970d8e33e01877950586

See more details on using hashes here.

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