/concise

CONCISE (COnvolutional neural Networks for CIS-regulatory Elements)

Primary LanguageJupyter NotebookMIT LicenseMIT

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Concise: Keras extension for regulatory genomics

Build Status license

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.

Installation

Concise is available for Python versions greater than 3.4 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

Documentation