- Python == 3.6.13
- opencv-python == 4.5.1.48
- tensorflow-gpu == 1.14.0
- scikit-learn == 0.24.0
- pandas == 1.1.5
- numpy == 1.19.5
- matplotlib == 3.3.4
- h5py == 2.10.0
- Bio == 1.3.9
Dependencies can be installed using the following command:
conda create -n DeepmRNALoc python=3.6.13
conda activate DeepmRNALoc
pip install -r requirements.txt- CUDA == 10.0 (This is just a suggestion to make sure your program works properly)
- how to install CUDA and cuDNN:
conda install cudatoolkit=10.0
conda install cudnn=7.6.5
You can run it from the command line
feature extract:
cd ./DeepmRNALoc
python extract_feature.py
Tips: It might take a long time.
train and test:
python main.py --model [modelname, default = DeepmRNALoc] --train
only test:
python main.py --model [modelname, default = DeepmRNALoc]
Tips: Please check the root path before run the main.py .
inference:
python inference.py
For more parameter information, please refer to main.py.
We deployed a trained model on a dedicated server, which is publicly available at https://www.peng-lab.org:8080/mRNA/, to make it easy for biomedical researcher users to utilize DeepmRNALoc in their research activity.
Users can upload their mRNA sequences to the server, and then they can quickly obtain the predicted results of the mRNA subcellular localization.
DeepmRNALoc's five-fold cross-validation accuracies were 0.895, 0.594, 0.308, 0.944, and 0.865 in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus, respectively.
Wang S, Shen Z, Liu T, Long W, Jiang L, Peng S. DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning. Molecules. 2023; 28(5):2284. https://doi.org/10.3390/molecules28052284
If you have any questions, please feel free to contact Shihang Wang (Email: [email protected]) or Zhehan Shen (Email: [email protected]).
Pull requests are highly welcomed!
Thanks to Thales Institute and Shanghai Ocean University for providing computing infrastructure.
Thank you all for your attention to this work.