RNA-editing prediction using RF and biLSTM This project contains the source code created and used for the study and prediction of RNA-editing using Random Forest and Neural Networks as described in Zawisza et al. 2023.
- The src folder contains the c++ source code for the programs used to create the datasets for both Random Forest and Neural Networks approaches. (for help, contact: m.zawisza@ub.edu)
- Tha bash folder contains auxiliary bash scripts. (for help, contact: m.zawisza@ub.edu)
- biLSTM folder contains code for bidirectional LSTM deep learning approach, developed in python with tensorflow.
- RF folder contains code for Random Forests, developed in R.