Code for project "Overview of Biomedicine" under lab course "Deep Learning for Computer Vision and Biomedicine" - Technical University of Munich, Germany. Depends on Python 3, numpy, pytorch, scikit-learn, Bio, RNA, matplotlib.
Our paper: Binh Thanh Do, Vladimir Golkov, Göktuğ Erce Gürel, and Daniel Cremers, "Precursor microRNA Identification Using Deep Convolutional Neural Networks"
To set up the environmental variable in Mac OSX, follow the steps:
- Open
~/.bash_profile
with a text editor. - Add the line
export VIENNA_PATH="the-path-of-your-viennarna-package"
and save. - Restart the terminal, check if it is working by typing
echo $VIENNA_PATH
An example: export VIENNA_PATH="/Users/peace195/ViennaRNA/lib/python3.6/site-packages"
python filename.py
where:
File / Folder | Description |
---|---|
dataset/ | human , cross-species and new RNA sequences |
results/ | running log of each epoch in testset |
weights/ | saved model parameters |
cv.py | 5-fold cross validation for human and cross-species dataset using fixed-sized inputs ConvNet architecture |
test.py | Train and test in human and cross-species dataset with fixed-sized inputs ConvNet architecture |
test_new.py | Train and test in new dataset with fixed-sized inputs ConvNet architecture |
cv_variable_size.py | 5-fold cross validation for human and cross-species dataset using variable-sized inputs ConvNet architecture |
test_variable_size.py | Train and test in human and cross-species dataset with variable-sized inputs ConvNet architecture |
test_new_variable_size.py | Train and test in new dataset with variable-sized inputs ConvNet architecture |
utils.py | Read sequences, encode sequences and measurements |
ConvNet.py | Some ConvNet architectures such as Alexnet, Resnet, etc. |
statistics.py | Statistics of dataset |