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.

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.

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.

Phages, viruses that infect bacteria, offer a promising strategy against antibiotic-resistant pathogens. Phage viral proteins (PVPs) are essential for phage-host interactions, yet their identification and functional annotation remain challenging due to high sequence diversity, limited experimental data, and class imbalance. To address these issues, we propose ProtPhage, a novel framework that leverages the ProtT5 protein language model for richer sequence representation compared to traditional methods. Additionally, ProtPhage incorporates an asymmetric loss function to mitigate class imbalance, significantly improving the prediction of the minority class "minor capsid," with an F1 score 33.07$\%$ higher than the best existing model. Extensive experiments demonstrate that ProtPhage outperforms current methods across multiple metrics, including accuracy, precision, recall, and F1 score. A case study on the Mycobacterium phage PDRPxv genome further validates its practical utility, while expanded experiments highlight its potential in phage-host prediction. By integrating advanced deep learning techniques, ProtPhage establishes a new standard for PVP identification and annotation, contributing to the broader field of computational phage biology.