摘要
基因调控网络(Gene Regulatory Network,GRN)推断对于解析细胞发育机制及推动精准医学研究至关重要,但是现有深度学习方法面临计算复杂度高与全局特征捕捉不足的挑战。为此,提出一种融合Kolmogorov-Arnold网络(KAN)驱动的图卷积网络(KGCN)与Mamba模块的高效预测模型。首先,以KAN特有的可学习样条函数,取代图卷积网络中的多层感知器(MLP)模块。该改进在完整保留邻居节点局部特征提取能力的基础上,通过重构计算逻辑降低特征处理的冗余度,使模型计算复杂度较传统图卷积架构实现显著优化。其次,创新性地引入Mamba模块,通过其选择性机制优先关注对全局调控起关键作用的基因节点。两者结合实现了局部特征提取效率与全局依赖建模能力的协同优化。在公开数据集上与另外6种深度学习模型进行实验比较,结果显示,该模型在AUC和AUPR性能指标上都优于其他模型,同时展现出显著的鲁棒性优势和计算效率。
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.
作者
高泰
任艳璋
王会青
李颖
王彬
GAO Tai;REN Yanzhang;WANG Huiqing;LI Ying;WANG Bin(College of Computer Science and Technology(College of Data Science),Taiyuan University of Technology,Jinzhong,Shanxi 030600,China)
出处
《计算机科学》
北大核心
2026年第4期101-111,共11页
Computer Science
基金
国家自然科学基金(62176177)
山西省科技合作交流专项项目(202304041101034)。
