Metadata-Version: 1.1
Name: concise
Version: 0.6.0
Summary: CONCISE (COnvolutional Neural for CIS-regulatory Elements) is a model for predicting PTR features like mRNA half-life from cis-regulatory elements using deep learning. 
Home-page: https://github.com/avsecz/concise
Author: Žiga Avsec
Author-email: avsec@in.tum.de
License: MIT license
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
        
        ## 
        
        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 utilites: concise provides custom `layers`, `initializers` and `regularizers` useful for regulatory genomics
        3. tune the hyper-parameters (`hyopt`): concise provides convenience functions for working with `hyperopt` package.
        4. interpret: concise layers contain visualization 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
        
        <!-- TODO - include image of concise -->
        
        
        ## Installation
        
        Concise is available for python versions greater than 3.4 and can be installed from PyPI using `pip`:
        
        ```sh
        pip install --process-dependency-links concise
        ```
        
        `--process-dependency-links` is required in order to properly install the following github packages: [deeplift](https://github.com/kundajelab/deeplift) and [simdna](https://github.com/kundajelab/simdna/tarball/0.2#egg=simdna-0.2).
        
        <!-- 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
