Efficient Protein Structural Class Prediction Via Chaos Game Representation and Recurrent Neural Networks

Predicting the structural class of a protein from its amino acid sequence is among the most significant problems in bioinformatics, especially for proteins with a low sequence similarity. While current methods using recurrent neural networks achieve a notable accuracy in this task, their approach relies on extracting a large quantity of features, impacting the efficiency and reliability of the prediction. In this work, we introduce an efficient and accurate classification scheme based on chaos game representation and recurrent neural networks. The proposed scheme achieves comparable results with state-of-the-art methods, while using a significantly lower-dimensional representation of the feature space.