Metadata-Version: 2.1
Name: immuneML
Version: 1.1.4
Summary: immuneML is a software platform for machine learning analysis of immune receptor repertoires.
Home-page: https://github.com/uio-bmi/immuneML
Author: immuneML Team
Author-email: milenpa@student.matnat.uio.no
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: pytest (>=4)
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Requires-Dist: scikit-learn (>=0.23)
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Requires-Dist: airr (>=1)
Requires-Dist: pystache (==0.5.4)
Requires-Dist: torch (==1.5.1)
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Provides-Extra: tcrdist
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# immuneML

![Python application](https://github.com/uio-bmi/immuneML/workflows/Python%20application/badge.svg?branch=master)
![Docker](https://github.com/uio-bmi/immuneML/workflows/Docker/badge.svg?branch=master)

immuneML is a software platform for machine learning analysis of immune receptor sequences.

It supports the analyses of experimental B- and T-cell receptor data,
as well as synthetic data for benchmarking purposes.

In immuneML, users can define flexible workflows supporting different
machine learning libraries (such as scikit-learn or PyTorch), benchmarking of different approaches, numerous reports
of data characteristics, ML algorithms and their predictions, and
visualizations of results.

Additionally, users can extend the platform by defining their own data
 representations, ML models, reports and visualizations.


To get started see the documentation at https://docs.immuneml.uio.no.

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© Copyright 2021, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Victor Greiff, Geir Kjetil Sandve




