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DeepmRNALoc: A novel predictor of eukaryotic mRNA subcellular localization based on deep learning

Python 3.6 License CC BY-NC-SA

Requirements

  • 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

Usage For AI Researchers

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.

Usage For Biomedical Researchers

We deployed a trained model on a dedicated server, which is publicly available at http://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.

Results

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.

Citation

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

Contact

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!

Acknowledgments

Thanks to Thales Institute and Shanghai Ocean University for providing computing infrastructure.
Thank you all for your attention to this work.

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