Cancer subtype classification requires capturing complex multi-omics interactions that conventional machine learning models often struggle to represent effectively. Graph Convolutional Networks (GCNs) leverage biological topologies but typically rely on fixed propagation mechanisms, which limit adaptability to diverse graph structures. This study introduces the Dynamic Bernstein Graph Convolutional Network (DB-GCN), a novel architecture that employs adaptive spectral propagation using Bernstein polynomials to enable topology-aware learning without eigendecomposition. DB-GCN supports single-omics (RNA) and multi-omics (RNA+CNV) inputs within a graph-based framework that represents genes as nodes and interactions as edges derived from gene-gene, protein-protein, and Co-expression networks. A dual-stream design combines a Bernstein graph stream with an omics multilayer perceptron to capture both local and global features. In pan-cancer experiments on 28 TCGA subtypes, DB-GCN achieves 86.05% $\pm$ 0.83 on STRING, 85.86% $\pm$ 0.98 on BioGRID, and 85.88% $\pm$ 0.71 on Co-expression for the 2,000-gene multi-omics setting. SHAP-based analysis identifies putative biomarker genes such as KLK11, OR4F15, and UBE2DNL, and 12 of the top 50 genes map to KEGG cancer pathways. These results indicate that DB-GCN provides an accurate and interpretable graph-based framework for pan-cancer subtype classification and biomarker discovery in precision oncology.
Automatic question-answering systems demonstrate valuable utility in the biomedical domain, improving the precision and efficiency of clinical decision-making significantly. Despite large-scale language models achieving notable success in general domains, even outperforming human-level performance in certain aspects, they are still faced with challenges such as data privacy and scarcity in the special domain. This study explores the method for efficient fine-tuning under resource-constrained conditions in the biomedical field. We propose a multi-stage fine-tuning approach that effectively improves the performance of pre-trained language models in biomedical question-answering tasks. Specially, A multi-prompt-based contrastive learning strategy and a multi-prompt self-consistency voting module are introduced, which improve the accuracy of QA tasks. The experiments on the PubMedQA dataset under reasoning-required settings indicate that our approach outperforms domain-specific pre-training models and achieves comparable performance with GPT-4, while the number of fine-tuned parameters is much less than the total parameters of the base model.
Drug combination therapy for complex diseases is currently widely utilized in clinical treatment. An increasing number of computational methods are being adopted to discover new drug combinations. However, most existing methods mainly focus on phenotypes, while ignoring the deep level interactions between drug pairs and cell lines. In this paper, we introduce a multimodal hypergraph representation learning approach named MHGSynergy, aimed at predicting drug synergies. MHGSynergy models the synergistic relationship as a hypergraph by considering the structure, targets, and physicochemical features of drugs. The nodes represent drugs and cell lines, and hyperedges capture synergistic triplets of drug pairs and cell lines. Different drug features are used as attributes of drug nodes in the hypergraph to construct three hypergraphs. Hypergraph neural networks are employed to update embedding features of drugs and cell lines, then input these embedding features into the channel attention fusion module to obtain a comprehensive representation. Finally, these representations are used to build predictive models. In both classification and regression tasks, MHGSynergy performs well on two benchmark datasets, and outperforming those baseline methods, and MHGSynergy has unique advantages when facing unknown drug pairs or cell lines. We believe that MHGSynergy is a valuable tool for discovering synergistic drug combinations.
Classifying drugs into symptomatic (SYM) and disease-modifying (DM) categories is essential for understanding their therapeutic effect and plays a key role in drug repurposing. While many computational approaches focus on drug-target prediction, they often ignore the nature of the drug's action on disease progression. This study proposes a graph-based strategy to classify drugs as SYM or DM based on their effect on disease treatment. We construct a heterogeneous network comprising genes, diseases, and drugs, and apply a guided shortest path traversal framework for drug effect classification. During this traversal, certain genes appear frequently in the shortest metapaths linking diseases and drugs. These recurrent genes are identified based on their frequency of occurrence in known drug-disease paths. For a new drug-disease pair, if the traversal path contains recurrent genes marked for a specific treatment type, we classify the drug accordingly. Over and above classifying the drugs, the proposed method incorporates the metapath-based framework to improve interpretability. Experimental results show that our model achieves significantly better classification accuracy compared to advanced machine learning and deep learning methods. A case study on multiple sclerosis further supports the biological relevance of our approach.
Compared with monotherapy, combination drug therapy plays a crucial role in clinical treatment. However, the exponential expansion of the drug combination space has rendered traditional exploration methods for synergistic drug combinations inadequate. Recently, numerous efficient and accurate computational approaches have been developed to predict anticancer drug synergy, particularly those leveraging hypergraphs to model the multifaceted relationships between drug combinations and cell lines, which have demonstrated remarkable potential. Nevertheless, existing hypergraph-based methods fail to account for the heterogeneity of anticancer synergy hypergraphs and overlook the underlying similarities among drugs and cell lines, thereby limiting their ability to fully capture the complex interactions between drug combinations and cell lines. To address these limitations, we propose an Adaptive Heterogeneous Hypergraph Representation Learning Method (HHGSynergy) for predicting anticancer drug synergy, enabling more precise identification of synergistic drug combinations. Specifically, our framework first constructs drug/cell line similarity-based synergy hypergraphs based on the foundational anticancer synergy hypergraph, thereby establishing a comprehensive heterogeneous hypergraph. Next, a node importance calculation module is employed to learn both local and global importance weights of nodes, effectively capturing the structural characteristics of the hypergraph. Finally, a type-specific multi-head attention mechanism is utilized to iteratively update node embeddings, adaptively learning the significance of heterogeneous hyperedges. Experimental results demonstrate that HHGSynergy achieves state-of-the-art performance in both classification and regression tasks across diverse experimental scenarios, outperforming existing leading models. Case studies further underscore its potential for discovering novel synergistic drug combinations.
