Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless networks, enabling simultaneous high-speed communication and precise environmental awareness. This paper presents a novel ISAC imaging method, which leverages sparse multi-view channel state information (CSI) from existing communication infrastructure to reconstruct scattering fields, thereby achieving high-fidelity 3D imaging and environment reconstruction without the need for dedicated sensing hardware. A Neural Scattering Field Network (NSFNet) is designed to accomplish this task. The framework consists of two key components: 1) EdgeFusionNet, which extracts robust geometric features from sparse multi-view CSI using a multi-scale 3D CNN with edge-guided attention, and 2) MLP-based decoder that explicitly regresses view-dependent scattering coefficients, thereby addressing both the limited-view sampling challenge and the physical view-dependency of scattering. A self-supervised training strategy combining reconstruction loss and total variation regularization ensures accurate and smooth reconstructions. Experimental results demonstrate that NSFNet significantly outperforms compressed sensing and ablation deep learning baselines in complex scenarios, achieving superior performance in terms of F1-score (>0.83) and Chamfer Distance (<0.15 m). Furthermore, the method maintains stable performance under practical signal-to-noise ratio conditions and varying user equipment deployment densities, offering a scalable and hardware-efficient solution for ISAC-enabled environmental sensing. The proposed approach bridges the gap between sparse communication channel measurements and high-resolution 3D imaging, paving the way for seamless integration of sensing and communication in next-generation wireless networks.

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 GRNs based on gene expression data has been a central computational problem in systems biology. However, due to the high dimensionality and non-linearity of large-scale GRNs, accurately and efficiently inferring GRNs is still a challenging task. RESULTS: In this article, we propose a new approach, iLSGRN, to reconstruct large-scale GRNs from steady-state and time-series gene expression data based on non-linear ordinary differential equations. Firstly, the regulatory gene recognition algorithm calculates the Maximal Information Coefficient 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 non-linear ordinary differential equations 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.