Prediction of Protein-Protein Interactions with LSTM Deep Learning Model

Protein-protein interactions (PPI) has a vital role in molecular biology and bioinformatics since they are the key organisms which give information about cellular, its structure and its functions. In recent years many methods and techniques are proposed in order to perform PPI's yet they are suffered from operational time, and large costs as well as low prediction accuracy. In this study, we performed a deep learning approach to resolve these problems. To do that we introduced a LSTM architecture to predict protein-protein interactions by applying both ProtVec and protein signatures methods. VCP (valosin-containing protein) which is associated with H. Pylori is considered in this work. The performance of the method determined by log-loss, ROC, and classification accuracy. The proposed method showed a good predictive ability yet there is still more works need to be performed to improve the results of PPI prediction studies with respect to deep learning and machine learning approaches.