iDeep-Cancer: Predicting Cancer-Related circRNA-RBP Binding Sites Using a Hybrid Network Framework

Interactions among circular RNAs (circRNAs) and RNA-binding proteins (RBPs) involve almost all stages of the circRNA life cycle. Therefore, circRNA-RBP binding site identification is extremely important for the regulation of human diseases. Various approaches have been used to identify RBP binding sites on circRNAs. Sadly, these approaches are frequently constrained by insufficient feature learning and poor scalability. As a result, we provide a novel model named iDeep-cancer that predicts circRNA-RBP interactions solely using circRNA sequences. A hybrid deep learning model and feature encoding are used in the iDeep-cancer technique. In order to create the feature space, feature encoding uses four feature extraction techniques while accounting for the chemical makeup of circRNA sequences. The hybrid network includes an improved dense convolutional network (DenseNet), a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (Self-attention). DenseNet is used to learn high-level localized features, and the combination of BiGRU and self-attention captures long-term dependencies in sequences. We conducted ablation tests and compared iDeep-cancer with other cutting-edge techniques on 13 datasets in order to verify its efficacy. Findings indicate that iDeep-cancer performs better than current techniques.