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This project is intended for study of implementing deep learning to a binary classification of sgRNA off-target prediction. Five different models were compared in this study to get the best performance in classification prediction for off-target sgRNA. All models implement Word2Vec embedding to get better feature vector representation compared to the traditional one-hot encoding method. The five models were constructed by different RNN models: biLSTM, LSTM, GRU, biGRU, and without RNN Layer (NoRNN). Each model was trained with a learning rate default of Adam optimizer 0.001 and tested with two different datasets: HEK293T dataset and K562 Dataset.

Paper has been published on IEEE and presented on ICCoSITE 2023 Conference

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The work of this project aims to study the application of machine learning in the field of bioinformatics, especially Drug Discovery. This project was created by following a tutorial from Chanin Nantasenamat. Then this project continued for the Natural Language Processing coursework at Binus University by fine-tuning the dataset with the Transformers model (ChemBERTa).

Paper has been published on IEEE and presented on ICITISEE 2023 Conference

Check this repository for the full project