CCS Model Training and Prediction

Publication:

• doi: https://doi.org/10.1101/2020.05.19.102285
• biorxiv: https://www.biorxiv.org/content/10.1101/2020.05.19.102285v1

Library Setup

Setup CUDA 10.0 with cudnn and install the required python libraries with pip:

pip install -r requirements.txt

Prediction with Pre-Trained Model

Unzip the checkpoint found in out.

Prepare a csv file that contains Sequence and Charge Information and use the provided predict.py script:

python predict.py <filename.csv> 

For the format see the provided example file in ./data/combined_reduced.csv

Process data

Use the provided notebook: process_data_final.ipynb

It uses the raw data files and saves train and test files in pkl format to disc in ./data_final

Training

The bidirectional_lstm.py file contains training and prediction routines.

Training is done by setting the paths in run_training.py and executing it. The complete dataset will be uploaded at a later stage of publication.

Evaluation

Use the provided evaluate.ipynb Jupyter Notebook.