https://github.com/harvardnlp/regulatory-prediction
Code and Data to accompany "Dilated Convolutions for Modeling Long-Distance Genomic Dependencies", presented at the ICML 2017 Workshop on Computational Biology
https://github.com/harvardnlp/regulatory-prediction
computational-biology convolutional-neural-networks deep-learning recurrent-neural-networks
Last synced: 6 months ago
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Code and Data to accompany "Dilated Convolutions for Modeling Long-Distance Genomic Dependencies", presented at the ICML 2017 Workshop on Computational Biology
- Host: GitHub
- URL: https://github.com/harvardnlp/regulatory-prediction
- Owner: harvardnlp
- License: bsd-3-clause
- Created: 2017-06-06T14:46:07.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-10-03T07:05:59.000Z (about 8 years ago)
- Last Synced: 2025-04-01T15:49:03.032Z (6 months ago)
- Topics: computational-biology, convolutional-neural-networks, deep-learning, recurrent-neural-networks
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 28
- Watchers: 9
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# regulatory-prediction
Code and Data to accompany "Dilated Convolutions for Modeling Long-Distance Genomic Dependencies", presented at the ICML 2017 Workshop on Computational Biology, by Ankit Gupta and Alexander Rush.
Data forthcoming. Please email ankitgupta@college.harvard.edu if you have any questions in the meantime.
## Current State
Ankit is current working on a unified set of deep learning benchmarks for genomics tasks. All of these results and a cleaned up version of the code will be included with that repository, and it will be linked here. In the meantime, feel free to email us if you have any questions.
## Dependencies
- Python 2.7 (3.x should also work but not thoroughly tested)
- Tensorflow 1.0
- Numpy 1.12.0
- Scipy 0.17.1
# Usage
Run `python train.py`