Deep-learning-for-contact_map_v2

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Deep-learning-for-contact_map_v2

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Prediction of protein contact map

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# Deep-learning-for-contact\_map\_v2

This is an **unofficial** implementation of [Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005324), which is a deep learning method for prediction of pretein contact map, with predicted contact map by other software(for example CCMpred, PSICOV, and so on) as input.

## Requirements

- [python2.7]()
- [Tensorflow (>= 1.0.0)](https://www.tensorflow.org/install/install_linux)
- [Numpy](https://github.com/numpy/numpy/blob/master/INSTALL.rst.txt)

## Introduction
- Network structure: 2 networks(residual network and highway network) were implemented;
- Batch normalization and L2 regulation were implemented for optimization.

## Need to do
1. Get protein structure 1D features(for example sequence, sse, ACA, and so on), and 2D features(for example predicted CCMpred, PSICOV and other pairwise features)
2. Modify `./read_into_tfrecord.py`, and used it to transfer your data to tfrecord
3. set your own config in `./libs/config/config.py`
4. run `python train.py`

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