Bioinformatics. 2023 Oct 18:btad619. doi: 10.1093/bioinformatics/btad619. Online ahead of print.
MOTIVATION: Gene regulatory networks (GRNs) are a way of describing the interaction between genes, which contribute to revealing the different biological mechanisms in the cell. Reconstructing gene regulatory networks based on gene expression data has been a central computational problem in systems biology. However, due to the high dimensionality and nonlinearity of large-scale gene regulatory networks, accurately and efficiently inferring gene regulatory networks is still a challenging task.
RESULTS: In this paper, we propose a new approach, iLSGRN, to reconstruct large-scale GRNs from steady-state and time-series gene expression data based on nonlinear ordinary differential equations (ODEs). Firstly, the regulatory gene recognition algorithm calculates the Maximal Information Coefficient (MIC) between genes and excludes redundant regulatory relationships to achieve dimensionality reduction. Then, the feature fusion algorithm constructs a model leveraging the feature importance derived from XGBoost (eXtreme Gradient Boosting) and RF (Random Forest) models, which can effectively train the nonlinear ODEs model of GRNs and improve the accuracy and stability of the inference algorithm. The extensive experiments on different scale datasets show that our method makes sensible improvement compared with the state-of-the-art methods. Furthermore, we perform cross-validation experiments on the real gene datasets to validate the robustness and effectiveness of the proposed method.
AVAILABILITY AND IMPLEMENTATION: The proposed method is written in the Python language, and is available at: https://github.com/lab319/iLSGRN.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.