FSKansformer: An ncRNA-Protein Interaction Prediction Model Based on Feature Salience and Kansformer

The interaction between non-coding RNA (ncRNA) and protein (ncRPI) plays a crucial role in many physiological activities and disease progression. To identify ncRPIs on a large scale by computational methods based on deep learning is a common practice. However, existing computational methods face challenges such as low feature expression and redundant suppression capability when processing high dimensional feature data. To this end, we propose a new prediction model, called FSKansformer, in which a feature salience module is introduced to highlight useful information and suppress noise of multi-view feature matrices. In order to reduce the loss of feature information caused by serial extraction of global feature and local feature, we propose an parallel extraction framework in which a improved Kansformer is designed to extract global high-dimensional features, BiLSTM and LSTM techniques are used to extract local high-dimensional features simultaneously. Finally, the fused global-local high-dimensional features are input into the three-layer KAN network for dimensionality reduction to generate the final prediction score. Experiment results show that FSKansformer achieves state-of-the-art performance on five benchmark datasets compared with other models.