Metadata-Version: 2.4
Name: adpred
Version: 1.3.2
Summary: Prediction of Transcription Activation Domains from protein sequences
Home-page: https://github.com/FredHutch/adpred-pkg
Author: Ariel Erijman
Author-email: aerijman@fredhutch.org, aerijman@neb.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: keras==3.11.3
Requires-Dist: numpy==2.3.3
Requires-Dist: pandas==2.3.2
Requires-Dist: plotly==6.3.0
Requires-Dist: requests==2.32.5
Requires-Dist: requests-oauthlib==2.0.0
Requires-Dist: scikit-learn==1.7.2
Requires-Dist: tensorflow==2.20.0
Dynamic: author
Dynamic: author-email
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[![License](http://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/nebiolabs/tasmanian/blob/master/LICENSE)
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# ADpred  

A tool for prediction of Transcription Activation Domains from protein sequences.

<!--toc start-->
 * [Goals](#goals)
 * [Documentation](https://adored.readthedocs.io/en/latest/)
 * [Contributing](#contributing)
 * [Authors](#authors)
 * [Licence](#licence)
<!--toc end -->


## Goals  

The main goal is to identify regions with high AD function probability in protein sequences. Moreover, at these observed regions, a saturated mutagenesis study can reveal insights into the important residues that confer the AD function to that region.

## Contributing

Contributions are welcome and encouraged.

## [Documentation](https://adored.readthedocs.io/en/latest/index.html?highlight=predict)

## Authors
* [Ariel Erijman](https://github.com/aerijman)

## Licence
`ADpred` is an open source software released under the [MIT licence](#)
