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https://github.com/qdata/deepmotif
Deep Motif (ICLR16)/ Deep Motif Dashboard (PSB17): Visualizing Genomic Sequence Classifications
https://github.com/qdata/deepmotif
deep-learning genomics torch
Last synced: about 1 month ago
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Deep Motif (ICLR16)/ Deep Motif Dashboard (PSB17): Visualizing Genomic Sequence Classifications
- Host: GitHub
- URL: https://github.com/qdata/deepmotif
- Owner: QData
- License: mit
- Created: 2016-03-26T15:30:52.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-04-19T20:19:53.000Z (over 5 years ago)
- Last Synced: 2024-05-14T00:16:16.092Z (7 months ago)
- Topics: deep-learning, genomics, torch
- Language: Lua
- Homepage: http://www.deepmotif.org
- Size: 4.88 MB
- Stars: 44
- Watchers: 14
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
### Jack Lanchantin, Ritambhara Singh, Beilun Wang, and Yanjun Qi
### Pacific Symposium on Biocomputing (PSB) 2017
https://arxiv.org/abs/1608.03644
### Talk slides:
https://github.com/QData/DeepMotif/blob/master/psb_talk_slides.pdf
### bibtex:
```
@inproceedings{lanchantin2017deep,
title={Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks},
author={Lanchantin, Jack and Singh, Ritambhara and Wang, Beilun and Qi, Yanjun},
booktitle={PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017},
pages={254--265},
year={2017},
organization={World Scientific}
}
```
[](https://github.com/QData/DeepMotif/blob/master/LICENSE)
# Installation
## Lua setup
The main modeling code is written in Lua using [torch](http://torch.ch)
Installation instructions are located [here](http://torch.ch/docs/getting-started.html#_)
After installing torch, install / update these packages by running the following:
```bash
luarocks install torch
luarocks install nn
luarocks install optim
```
### CUDA support (Optional)
To enable GPU acceleration with [CUDA](https://developer.nvidia.com/cuda-downloads), you'll need to install CUDA 6.5 or higher as well as [cutorch](https://github.com/torch/cutorch) and [cunn](https://github.com/torch/cunn). You can install / update the torch CUDA libraries by running:
```bash
luarocks install cutorch
luarocks install cunn
```
## LFS
Install git large file storage (LFS) in order to download the dataset directly from this git repository.
https://git-lfs.github.com/
## Visualization Method Dependencies
Weblogo: http://weblogo.berkeley.edu/
R: https://www.r-project.org/
# Usage
## Step 1: Get the Data
tar xvzf data/deepbind.tar.gz -C data/
## Step 2: Train the model
You can train one of the 3 types of models (CNN, RNN, or CNN-RNN). Check the flags in main.lua for parameters to run the code.
For CNN model:
```bash
th main.lua -cnn
```
For CNN model:
```bash
th main.lua -rnn
```
For CNN-RNN model:
```bash
th main.lua -cnn -rnn
```
## Step 3: Visualize the Model's Predictions
Once you have trained models, you can visualize the predictions.
Saliency Map
```bash
th saliency_map.lua
```
Temporal Output Values
```bash
th temporal_output_values.lua
```
Class Optimization
```bash
th class_optimization.lua
```