concise 0.3.2

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.

===============================
CONCISE
===============================


.. image:: https://img.shields.io/pypi/v/concise.svg
:target: https://pypi.python.org/pypi/concise

.. image:: https://img.shields.io/travis/Avsecz/concise.svg
:target: https://travis-ci.org/Avsecz/concise

.. image:: https://readthedocs.org/projects/concise-bio/badge/?version=latest
:target: https://concise-bio.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status

CONCISE (COnvolutional neural Network for CIS-regulatory Elements) is a model for predicting any quatitative outcome (say mRNA half-life) from cis-regulatory sequence using deep learning.

* Developed by the Gagneur Lab (computational biology): https://www.gagneurlab.in.tum.de
* Free software: MIT license
* Documentation: https://concise-bio.readthedocs.io

.. image:: https://github.com/Avsecz/concise/blob/master/concise-figure1.png
:target: https://github.com/Avsecz/concise/blob/master/concise-figure1.png
:width: 60%
:align: center

Features
--------

* Very simple API
* Serializing the model to JSON

* allows to analyze the results in any langugage of choice

* Helper function for hyper-parameter random search
* CONCISE uses TensorFlow at its core and is hence able of using GPU computing

Installation
------------

After installing the following prerequisites:

1. Python (3.4 or 3.5) with pip (see `Python installation guide`_ and `pip documentation`_)
2. TensorFlow python package (see `TensorFlow installation guide`_ or `Installing Tensorflow on AWS GPU-instance`_)


install CONCISE using pip:

::

pip install concise


.. _pip documentation: https://pip.pypa.io
.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
.. _TensorFlow installation guide: https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html
.. _Installing Tensorflow on AWS GPU-instance: http://max-likelihood.com/2016/06/18/aws-tensorflow-setup/

Getting Started
---------------

.. code-block:: python

import pandas as pd
import concise

# read-in and prepare the data
dt = pd.read_csv("./data/pombe_half-life_UTR3.csv")

X_feat, X_seq, y, id_vec = concise.prepare_data(dt,
features=["UTR3_length", "UTR5_length"],
response="hlt",
sequence="seq",
id_column="ID",
seq_align="end",
trim_seq_len=500,
)

######
# Train CONCISE
######

# initialize CONCISE
co = concise.Concise(motif_length = 9, n_motifs = 2,
init_motifs = ("TATTTAT", "TTAATGA"))

# train:
# - on a GPU if tensorflow is compiled with GPU support
# - on a CPU with 5 cores otherwise
co.train(X_feat[500:], X_seq[500:], y[500:], n_cores = 5)

# predict
co.predict(X_feat[:500], X_seq[:500])

# get fitted weights
co.get_weights()

# save/load from a file
co.save("./Concise.json")
co2 = Concise.load("./Concise.json")

######
# Train CONCISE in 5-fold cross-validation
######

# intialize
co3 = concise.Concise(motif_length = 9, n_motifs = 2,
init_motifs = ("TATTTAT", "TTAATGA"))

cocv = concise.ConciseCV(concise_object = co3)

# train
cocv.train(X_feat, X_seq, y, id_vec,
n_folds=5, n_cores=3, train_global_model=True)

# out-of-fold prediction
cocv.get_CV_prediction()

# save/load from a file
cocv.save("./Concise.json")
cocv2 = ConciseCV.load("./Concise.json")



Where to go from here:
----------------------

* See the example file `<scripts/example-workflow.py>`_
* Read the API Documenation https://concise-bio.readthedocs.io/en/latest/documentation.html



=======
History
=======

0.1.0 (2016-09-15)
------------------

* First release on PyPI.

0.1.1 (2016-09-17)
------------------

* Minor documentation changes
* Renamed some internal variables

0.2.0 (2016-09-21)
------------------

* Introduced new feature: regress_out_feat
* Major renaming of variables for concistency

0.3.0 (2016-11-30)
------------------

* Added L-BFGS optimizer in addition to Adam. Use optimizer="lbfgs" in Concise()