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🧬 Protein Subcellular Localization Prediction

This repository contains a deep learning project designed to predict protein subcellular localization using neural networks (Dense Model, CNN, ResNet). The models are trained on protein sequences to classify them into 10 possible localization categories. Subcellular localization refers to the specific location or environment within a cell where a protein resides. Correctly identifying this is essential for understanding the protein's function and role in biological processes.

🔄Installation

Clone the repository

git clone git@github.com:zhukovanadezhda/subcellular-localization.git
cd subcellular-localization

Setup the conda environment

Install miniconda. Create the deep-learning conda environment:

conda env create -f environment.yml

Load the environment

conda activate deep-learning

💡Note: To deactivate an active environment, use:

conda deactivate

📄 References

Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H., & Winther, O. (2017). DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics (Oxford, England), 33(21), 3387–3395. https://doi.org/10.1093/bioinformatics/btx431