Effectively modeling multimodal spatial omics data is critical for understanding tissue complexity and underlying biological mechanisms. While spatial transcriptomics, proteomics, and epigenomics capture molecular features, they lack pathological morphological context. Integrating these omics with histopathological images is thus critical for comprehensive disease tissue analysis. However, substantial heterogeneity across omics, imaging, and spatial modalities poses significant challenges. Naive fusion of semantically distinct sources often leads to ambiguous representations. Additionally, the resolution mismatch between high-resolution histology images and lower-resolution sequencing spots complicates spatial alignment. Biological perturbations during sample preparation further distort modality-specific signals, hindering accurate integration. To address these challenges, we propose Graph-guided Representation of Omics and Vision with Expert Regulation for Adaptive Spatial Multi-omics Fusion (GROVER), a novel framework for adaptive integration of spatial multi-omics data. GROVER leverages a Graph Convolutional Network encoder based on Kolmogorov–Arnold Networks to capture the nonlinear dependencies between each modality and its associated spatial structure, thereby producing expressive, modality-specific embeddings. To align these representations, we introduce a spot-feature-pair contrastive learning strategy that explicitly optimizes the correspondence across modalities at each spot. Furthermore, we design a dynamic expert routing mechanism that adaptively selects informative modalities for each spot while suppressing noisy or low-quality inputs. Experiments on real-world spatial omics datasets demonstrate that GROVER outperforms state-of-the-art baselines, providing a robust and reliable solution for multimodal integration.

Accurate prediction of cyclist trajectories is essential for safe and reliable autonomous driving and intelligent transportation systems (ITSs) in complex traffic scenarios. To address the challenges posed by cyclists’ diverse intentions and non-linear motion patterns, we propose PhysiCycle, a novel multi-task learning framework that jointly predicts future trajectories and turning intentions. This framework integrates interpretable physical modeling with deep learning to enhance both prediction accuracy and behavioral consistency. Our model integrates a dual-path encoder to extract temporal motion cues and behavioral features, an intention classifier module, and a physically consistent decoder with bicycle kinematics consistency constraints. Experimental results on a real-world cyclist action dataset demonstrate that our method significantly outperforms baseline models in both intention classification and trajectory accuracy, achieving strong physical plausibility and generalization performance.

Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed Prototype-aware Graph Adaptative Aggregation for Spatial Multi-modal Omics Analysis (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA.

Predicting unknown transcription factor-target gene (TF-target gene) interactions based on gene regulatory networks (GRNs) is critical for understanding cellular functions and biological discovery. Existing computational methods focus on the statistic corelation and neglect the semantic information of biomedical knowledge. To address this, we propose BioLinkGPT, a novel framework that integrates the robust textual comprehension capabilities of Large Language Models (LLMs) with graph neural networks (GNNs) to capture structural information of GRN. BioLinkGPT leverages gene information and the known TF-target gene relationships from literature to predict unknown TF-target gene interactions and improve its performance via a two-stage instruction fine-tuning strategy. We construct a comprehensive GRN dataset focused on human infectious diseases, and experimental results show that BioLinkGPT significantly outperforms existing baselines in link prediction metrics. We validate the model's accurate identification of known regulatory relationships and find that BioLinkGPT successfully uncover potential regulatory interactions with high confidence and biological relevance. BioLinkGPT offers an efficient and precise computational tool to infer TF-target gene interactions, accelerating research on infectious disease mechanisms and drug target development.

Predicting gene regulatory relationship is crucial to reveal cancer mechanism and develop targeted therapies. However, current computational methods typically estimate gene-gene associations without regulatory directions and only based on statistical significance, often lacking accuracy after validation by biological experiments. Therefore, we introduce a pipeline named LiGRNet that constructs gene regulatory networks as signed directed graphs using large language models (LLMs) from the literature and predicts potential gene regulatory relationship with magnetic signed graph neural networks (MSGNNs). First, we teach LLM to extract the known gene regulatory relationships proved by biological experiments through prompt. Then, a signed directed graph was constructed and learnt by MSGNN with a link prediction framework based on complex-valued gene embeddings. The potential regulatory relationships were predicted with direction and sign. We apply our pipeline in colorectal cancer, liver cancer, and colorectal liver metastasis including$11,000,19,000$, and 1,300 literature, respectively. The pipeline achieve$84.5 \%, 80.7 \%, 93.3 \%$accuracy in predicting unknown gene regulatory relationship. External cell line data also provide evidence for the predicted gene regulation in these cancers.

mRNA expression is vital for understanding the mechanisms of breast cancer initiation and metastasis, with mRNA-based biomarkers closely linked to tumor grade, metastasis, and prognosis. However, gene expression analyses often suffer from batch effects, high inter-sample variability, and poor cross-cohort generalizability, which can obscure true biological signals. To overcome these limitations, we developed the Support Vector Gene Pair (SVP) algorithm, which emphasizes within-sample relative gene pair relationships rather than absolute expression levels. This design effectively mitigates inter-sample interference and enhances robustness across heterogeneous datasets. By in-corporating intra- and inter-sample variability, SVP accurately identifies informative gene pair features. Applied to 16 GEO datasets (852 samples) and one TCGA dataset, SVP discovered 23 breast cancer-related gene pairs and outperformed traditional Differentially Expressed Gene (DEG) and unadjusted statistical approaches. Comparative analyses showed consistent performance gains, with average improvements of 8% in AUC and 6% in PRC. SVP offers a robust and interpretable framework for breast cancer biomarker discovery, holding strong promise for translational applications in precision medicine.

Online shopping has experienced rapid development recently. However, compared to offline shopping, the return rate and complaint rate of online shopping are much higher, especially for online clothes shopping. In order to solve this problem, virtual fitting technology arises at the right moment, and 3D human modeling is a crucial part of virtual fitting technology. The reconstruction of the parametric 3D human models often faces challenges as long fitting time, low accuracy and fuzzy depth information. Although the reconstruction of nonparametric 3D human model has improved accuracy and detail to some extent, such models typically lack flexibility and controllability. Therefore, this paper reconstructs a high-precision parametric 3D human model by proposing a multi-view iterative registration strategy based on pose prior estimation, which is integrated with a nonparametric 3D human model based on implicit functions. The resulting model retains not only high-precision and detail but also high flexibility and controllability, which has achieved a good effect on the application of 3D virtual fitting. The proposed method is tested on the open-source 3D human body dataset multi-garment network (MGN). The Chamfer distance of the SMPL mannequin reconstructed from multiple views can reach 2.18[Formula: see text]cm and the per-vertical-error can reach 1.63[Formula: see text]cm.