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+# 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}
+}
+```
+
+[![LICENSE](https://img.shields.io/badge/license-MIT-brightgreen.svg)](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
+```
+
+
+