MRF-XGBLC: Large-scale gene regulatory network inference based on multi-model fusion

Gene regulatory networks (GRNs) are crucial for revealing gene interactions and understanding cellular biological mechanisms. However, the high dimensionality and nonlinearity of gene expression data make accurate inference and reconstruction of large-scale GRNs a core computational challenge in systems biology. This study introduces a novel approach, termed MRF-XGBLC, for the reconstruction of large-scale Gene Regulatory Networks (GRNs) utilizing steady-state and time-series gene expression data through nonlinear ordinary differential equations. Firstly, MRF-XGBLC uses the maximum information coefficient (MIC) for dimensionality reduction and eliminates redundant regulatory relationships by calculating the MIC between factors as a prior step in model processing. Furthermore, recognizing the superior performance of the Lasso-Cox model in survival analysis, the feature fusion algorithm of this paper incorporates a hybrid model of XGBoost (eXtreme Gradient Boosting), RF (Random Forest), and Lasso-Cox (Least Absolute Shrinkage and Selection Operator-Cox proportional hazards regression model) integration to effectively train the nonlinear ordinary differential equations, thus improving the accuracy and stability of the inference algorithm. Extensive experiments on datasets of varying sizes demonstrate significant improvements over state-of-the-art methods. Cross-validation experiments on real gene datasets confirm the robustness and effectiveness of MRF-XGBLC.