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.

MOTIVATION: Spatial transcriptomics (ST) enables the study of spatial heterogeneity in tissues. However, current methods struggle with large-scale, high-resolution data, leading to reduced efficiency and accuracy in detecting spatial domains. A scalable, precise solution is urgently needed. RESULTS: We present STAHD, a scalable and efficient framework for spatial domain detection in ST data. Combining a graph attention autoencoder with multilevel k-way graph partitioning, STAHD decomposes large graphs into compact subgraphs and generates low-dimensional embeddings. This improves computational efficiency and clustering accuracy. Benchmarks on human and mouse datasets show STAHD outperforms existing methods and accurately reveals spatially distinct tumor microenvironments and functional regions. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at: https://github.com/Little-Eel/STAHD.

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 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.

Knowledge Distillation (KD) transfers knowledge from a large pre-trained teacher network to a compact and efficient student network, making it suitable for deployment on resource-limited media terminals. However, traditional KD methods require balanced data to ensure robust training, which is often unavailable in practical applications. In such scenarios, a few head categories occupy a substantial proportion of examples. This imbalance biases the trained teacher network towards the head categories, resulting in severe performance degradation on the less represented tail categories for both the teacher and student networks. In this paper, we propose a novel framework called Knowledge Rectification Distillation (KRDistill) to address the imbalanced knowledge inherited in the teacher network through the incorporation of the balanced category priors. Furthermore, we rectify the biased predictions produced by the teacher network, particularly focusing on the tail categories. Consequently, the teacher network can provide balanced and accurate knowledge to train a reliable student network. Intensive experiments conducted on various long-tailed datasets demonstrate that our KRDistill can effectively train reliable student networks in realistic scenarios of data imbalance.

In recent years, computer-aided diagnosis (CAD) systems employing convolutional neural networks (CNNs) have achieved remarkable performance in medical image classification tasks. Despite this, deploying CNN-based CAD systems on medical equipment presents challenges due to their enormous computational and storage resource requirements. In this case, knowledge distillation reduces the cost of deploying CNNs by guiding a lightweight student network to learn from a robust teacher network. However, medical images have higher inter-class similarity than natural images, which makes it difficult for the teacher network to provide clear and accurate classification knowledge to the student network, resulting in the performance degradation of the student network. To address this problem, we divide the teacher predictions into clear predictions, ambiguous predictions, and misclassified predictions to analyze the interference caused by the similarity of medical images on knowledge distillation and propose a novel knowledge distillation frame-work, termed ClearKD. By enhancing ambiguous predictions and misclassified predictions with clear predictions as a reference, our ClearKD consistently provides high-quality teacher classification knowledge to the student network, increasing the ability of the student network to distinguish medical images. The experimental results on the skin lesions classification datasets (ISIC2019) and the brain tumor dataset demonstrate that our ClearKD outperforms existing state-of-the-art knowledge distillation methods in medical image classification tasks.

Knowledge distillation(KD) aims to obtain a lightweight student network with the target dataset's pre-trained network(s). In practical applications, the student network distilled on one dataset may fail to make fine-grained classifications of multiple categories(such as birds and dogs). To this end and to make better use of various datasets' pre-trained models, knowledge amalgamation (KA) strives to integrate the knowledge of multiple expert models trained on different datasets to attain a student network with multi-expert knowledge. Proposed KA methods for image classification ignore the problem that teacher networks may encounter with untrained class samples and provide misleading guidance to the student network. To address this problem, we propose a knowledge amalgamation framework based on uncertainty suppression. A series of experiments demonstrate the effectiveness of our framework; some of the experiments yield an accuracy improvement of 2% compared to the proposed methods.

Knowledge distillation aims to improve the generalization capacity of the student model by transferring knowledge from the teacher model. Existing feature-based methods explore knowledge transfer through hand-crafted feature mappings between teacher-student pairs. However, in different layers, the knowledge volume varies, and the knowledge exhibits semantic gaps. This leads to the possibility that hand-crafted layer associations may not enable the student model to effectively learn knowledge from the teacher model. We address this problem from two angles. On one hand, to ensure maximum knowledge transfer, we propose adaptive feature mapping based on the effective receptive field, which can quantify the knowledge volume of different layers and thus establish the optimal knowledge transfer paths between teacher-student pairs. On the other hand, to enhance the student model's ability to learn knowledge with semantic gaps from the teacher model, we propose adaptive feature fusion that fuses multiple intermediate layers of the teacher model as additional supervision. Experimental results demonstrate that the proposed method can significantly improve the performance of the student model.