Concise (originally CONvolutional neural networks for CIS-regulatory Elements) allows you to:

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

Concise is tested for python versions 3.5 and 3.6 and can be installed from PyPI using pip:

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

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