https://github.com/const-ae/neural_network_dna_demo
Jupyter Notebook to demonstrate the use of Neural Networks for Transcription Factor Binding Site Prediction
https://github.com/const-ae/neural_network_dna_demo
demo dna ipython neural-network
Last synced: 14 days ago
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Jupyter Notebook to demonstrate the use of Neural Networks for Transcription Factor Binding Site Prediction
- Host: GitHub
- URL: https://github.com/const-ae/neural_network_dna_demo
- Owner: const-ae
- License: mit
- Created: 2017-05-15T14:46:40.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-06-20T10:51:38.000Z (almost 8 years ago)
- Last Synced: 2025-03-23T20:37:01.842Z (about 1 month ago)
- Topics: demo, dna, ipython, neural-network
- Language: Jupyter Notebook
- Homepage:
- Size: 3.21 MB
- Stars: 12
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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 /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).