Neural_Network_DNA_Demo

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Neural_Network_DNA_Demo

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Jupyter Notebook to demonstrate the use of Neural Networks for Transcription Factor Binding Site Prediction

// repository documentation

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# Neural Network for Sequence Data

This project is an example how the recent advancements in Neural Networks
can be applied to sequence data, namely DNA.

We will take a collection of sequences plus information if they are 
bound by a transcription factor as input data and after training
a convolutional neural network we will be able to make predictions 
for new sequences. In addition we will extract what the network learned
and make a plot of the motif.

The example are chosen such that it is not necessary to have a GPU and
 should learn just fine on a CPU.

# Preparation

To make sure everybody can play around with the example easily and you do not need to install the dependencies, please follow the instructions below.

The code for this tutorial is written in Python and you will need a 2.7 or 3.5 installation (https://www.python.org/downloads/). If you have

Neural Networks are a complex topic and there are quite a few packages you need to install to get going. The easiest way to install packages in Python is to use [Anaconda](https://www.continuum.io/downloads). In the following I will assume that you only have Anaconda installed.

## Libraries

To run the the code in the tutorial you will need the following libraries:

* Jupyter / IPython
* Keras
* Theano _or_ Tensorflow
* Numpy

## Installation Steps

After the successful installation of Anaconda we will create a new conda environment to not pollute the default environment:

```Shell
$ conda create -n seqnn python=3.5 
```

```Shell
# On Windows
$ activate seqnn
# On Mac / Linux
$ source activate seqnn
```

Install Theano

```Shell
$ conda install theano pygpu
```

Install other dependencies

```Shell
$ conda install scikit-learn keras numpy scipy matplotlib ipython jupyter pandas sympy nose nb_conda
```

Download the `Neural_Network_DNA_Demo` project either by cloning it with git

```Shell
$ git clone https://github.com/const-ae/Neural_Network_DNA_Demo.git
```

__OR__ download this [zip file](https://github.com/Artjom-Metro/Neural_Network_DNA_Demo/archive/master.zip) and extracting it somewhere.

Move with the command line to the `Neural_Network_DNA_Demo` folder:

```Shell
$ cd <LOCATION_OF_DOWNLOAD>/Neural_Network_DNA_Demo
```

and start the Jupyter notebook:

```Shell
$ jupyter notebook
```

A new browser page should open where you can click on the `.ipynb` file
and start experimenting.

# Problems, Issues etc.

If you have problems with the installation of ...

* ... Theano --> check [this](http://deeplearning.net/software/theano/install.html#install) guide
* ... Keras --> check [this](https://keras.io/#installation) guide

or just open an issue [here](https://github.com/const-ae/Neural_Network_DNA_Demo/issues).

Neural_Network_DNA_Demo

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