Metadata-Version: 1.1
Name: concise
Version: 0.6.5
Summary: CONCISE (COnvolutional Neural for CIS-regulatory Elements)
Home-page: https://github.com/gagneurlab/concise
Author: Žiga Avsec
Author-email: avsec@in.tum.de
License: MIT license
Description-Content-Type: UNKNOWN
Description: <div align="center">
            <img src="docs/img/concise_logo_text.jpg" alt="Concise logo" height="64" width="64">
        </div>
        
        
        # Concise: Keras extension for regulatory genomics
        
        [![Build Status](https://travis-ci.org/gagneurlab/concise.svg?branch=master)](https://travis-ci.org/gagneurlab/concise)
        [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/fchollet/keras/blob/master/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](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/>
        
        
        
Keywords: computational biology,bioinformatics,genomics,deep learning,tensorflow
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
