https://github.com/pfnet-research/bmi219-2017-proteinfolding
UCSF BMI219 Deep Learning (2017), Coding example (Prediction of protein folding with RNN and CNN)
https://github.com/pfnet-research/bmi219-2017-proteinfolding
bioinformatics chainer deep-learning
Last synced: about 1 year ago
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UCSF BMI219 Deep Learning (2017), Coding example (Prediction of protein folding with RNN and CNN)
- Host: GitHub
- URL: https://github.com/pfnet-research/bmi219-2017-proteinfolding
- Owner: pfnet-research
- Created: 2017-04-29T22:48:45.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-05-10T18:45:36.000Z (about 9 years ago)
- Last Synced: 2025-04-13T10:40:40.414Z (about 1 year ago)
- Topics: bioinformatics, chainer, deep-learning
- Language: Python
- Homepage: https://minicourses.ucsf.edu/bmi-219-deep-learning-2017
- Size: 44.9 KB
- Stars: 16
- Watchers: 1
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Protein secondary structure prediction with cascaded CNN and RNN
This is an example of the application of deep learning to protein secondary structure prediction.
This example is based on [1], but some minor modifications are applied.
See commentary.md for a detailed explanation
# Dependency
* [Chainer](http://chainer.org)
* [NumPy](http://www.numpy.org)
* [six](https://pypi.python.org/pypi/six)
# Usage
Retrieve dataset
```
bash get_data.sh
```
Train
```
PYTHONPATH="." python tools/train.py
```
# Reference
[1] Li, Z., & Yu, Y. (2016). Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks. arXiv preprint arXiv:1604.07176.