Ubiquitination Sites Prediction

This is the official repository of "A Benchmark for Machine Learning based Ubiquitination Sites Prediction from Human Protein Sequences" paper.

To Do

• Add end to end training codes.
• Add hybrid training codes.
• Add preprocessing codes.
• Add end to end demo code.
• Add hybrid demo code.
• Add end to end datasets.
• Add hybrid datasets.
• Update readme to show how to use the demo codes.
• Support to get raw sequences as the input of demo codes.
• Add benchmark dataset and its description.

Requirements

python 3.10
pytorch 2.1.0+cuda118

Install

For testing the project install the corresponding requirements.txt files in your environment.

If you want to use python environment:

1. Create a python environment: python3 -m venv <env_name>.
2. Activate the environment you have just created: source <env_name>/bin/activate.
3. Install dependencies inside it: pip3 install -r requirements.txt.

Demo

To do inference you have to prepare your windowed dataset and change the end_to_end_config.yaml. Then cd to the demo directory, cd ./demo and run the following command:

python inference_end_to_end.py

The result will be saved in the save_path directory of the yaml config file.

you have to convert your sequences to fixed size sequences, i.e., window size, similar to the provided file in data/test data/processed/window/55.csv.