concise 0.6.4

CONCISE (COnvolutional Neural for CIS-regulatory Elements)

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<img src="docs/img/concise_logo_text.jpg" alt="Concise logo" height="64" width="64">
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# Concise: Keras extension for regulatory genomics

[](https://travis-ci.org/gagneurlab/concise)
[](https://github.com/fchollet/keras/blob/master/LICENSE)

##

Concise (CONvolutional neural networks for CIS-regulatory Elements) is a Keras extension for regulatory genomics.

If allows you to:

1. Pre-process sequence-related data (say convert a list of sequences into one-hot-encoded numpy arrays).
2. Specify a Keras model with additional modules. Concise provides custom `layers`, `initializers` and `regularizers`.
3. Tune the hyper-parameters (`hyopt`): concise provides convenience functions for working with the `hyperopt` package.
4. Interpret: most of Concise layers contain plotting methods
5. Share and re-use models: every Concise component (layer, initializer, regularizer, loss) is fully compatible with Keras:
- saving, loading and reusing the models works out-of-the-box


## Installation

Concise is available for Python versions greater than 3.4 and can be installed from [PyPI](pypi.python.org) using `pip`:

```sh
pip install concise
```

To successfully use concise plotting functionality, please also install the libgeos library required by the `shapely` package:

- Ubuntu: `sudo apt-get install -y libgeos-dev`
- Red-hat/CentOS: `sudo yum install geos-devel`

<!-- Make sure your Keras is installed properly and configured with the backend of choice. -->

## Documentation

- <https://i12g-gagneurweb.in.tum.de/public/docs/concise/>