Abstract: Gene regulatory network(GRN) inference is pivotal for deciphering cell development mechanisms and propelling precisionmedicine research.However,existing deep learning approaches confront challenges of high computational complexity and inadequate global feature capture.To tackle this,a novel efficient prediction model integrating the Kolmogorov-Arnold network(KAN) driven graph convolutional network(KGCN) and Mamba module is proposed.Firstly,the multi-layer perceptron(MLP) in traditional graph convolution is replaced by KAN’s learnable spline functions,which retain local feature extraction while reducing redundancy through restructured computation,significantly improving efficiency.Secondly,the Mamba module is innovatively incorporated to prioritize attention to gene nodes critical for global regulation via its selective mechanism.Together,these components enable a unified optimization of both local and global feature modeling.Experimental comparisons with six other deep-learning models on public datasets are performed.Results demonstrate that this model outperforms others in AUC and AUPR performance metrics,while also showcasing remarkable robustness and computational efficiency,further demonstrating the superiority of the model.

Key words: Gene regulatory network, Deep learning, Kolmogorov-Arnold network, Graph convolutional network, Mamba