Acknowledgements
=================

A modified version of PARROT, created by Dan Griffith, was used to generate the network used for metapredict V3. The original implementation of PARROT was used to generate the V1 and V2 networks. See `https://pypi.org/project/idptools-parrot/ <https://pypi.org/project/idptools-parrot/>`_ for some very cool machine learning stuff. You can also check out the `PARROT paper <https://elifesciences.org/articles/70576>`_.

In addition to using Dan Griffith's tool for creating metapredict, the original code for ``encode_sequence.py`` was written by Dan.

We would like to thank the **DeepMind** team for developing AlphaFold2 and EBI/UniProt for making these data so readily available.

We would also like to thank the team at MobiDB for creating the database that was used to train metapredict V1. Check out their awesome stuff at `https://mobidb.bio.unipd.it <https://mobidb.bio.unipd.it>`_


Contributors 
---------------

We'd also like to thank the following folks who have contribute code, reported errors, and suggested changes.

* The Fried lab (broadly defined)
* Broder Schmidt 
* Sean Cascarina
* Keith Cheveralls
* Henrik Åhl for help with py3.12 compatibility.