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Qiao Ning

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JBHI Journal 2026 Journal Article

Graph Clustering-Guided Multi-View Neighborhood-Enhanced Graph Contrastive Learning for Drug-Target Interaction Prediction

Yaomiao Zhao
Shaohang Qiao
Qiao Ning
Minghao Yin

Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections. MneGCL first performs semantic clustering of drugs and targets by identifying strongly correlated nodes in the semantic similarity network to construct semantic contrastive prototypes, while simultaneously establishing phenotypic prototypes based on the Gaussian interaction profile kernel similarity. These complementary views are then combined through neighborhood-enhanced contrastive learning to effectively capture latent homogeneous features and enhance representation learning for sparse nodes in heterogeneous graphs, with final predictions generated through a graph autoencoders framework. Comparative experimental results demonstrate that MneGCL achieves superior performance across three benchmark datasets, with particularly notable improvements on the highly sparse DrugBank dataset, showing an average $2. 5 \%$ increase to baseline models. Additional experiments further validate the effectiveness of MneGCL in enriching feature representations for sparsely connected nodes.

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AIIM Journal 2025 Journal Article

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction

Qiao Ning
Yue Wang
Yaomiao Zhao
Jiahao Sun
Lu Jiang
Kaidi Wang
Minghao Yin

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions prediction are more popular in recent years. Conventional computational methods almost simply view heterogeneous network constructed by the drug-related and protein-related dataset instead of comprehensively exploring drug-protein pair (DPP) information. To address this limitation, we proposed a Double Multi-view Heterogeneous Graph Neural Network framework for drug-target interaction prediction (DMHGNN). In DMHGNN, one multi-view heterogeneous graph neural network is based on meta-paths and denoising autoencoder for protein-, drug-related heterogeneous network learning, and another multi-view heterogeneous graph neural network is based on multi-channel graph convolutional network for drug-protein pair similarity network learning. First, a meta-path-based graph encoder with the attention mechanism is used for substructure learning of complex relationships from heterogeneous network constructed by proteins, drugs, side-effects and diseases, obtaining key information that is easy to be ignored in global learning of heterogeneous networks, and multi-source neighbouring features for drugs and proteins are learned from heterogeneous network via denoising auto-encoder model. Then, multi-view graphs of drug-protein pairs (DPPs) including the topology graph, semantics graph and collaborative graph with shared weights are constructed, and the multi-channel graph convolutional network (GCN) is utilized to learn the deep representation of DPPs. Finally, a multi-layer fully connection network is trained to predict drug-target interactions. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

LBMKGC: Large Model-Driven Balanced Multimodal Knowledge Graph Completion

Yuan Guo
Qian Ma
Hui Li
Qiao Ning
Furui Zhan
Yu Gu
Ge Yu
Shikai Guo

Multi-modal Knowledge Graph Completion (MMKGC) aims to predict missing entities, relations, or attributes in knowledge graphs by collaboratively modeling the triple structure and multimodal information (e. g. , text, images, videos) associated with entities. This approach facilitates the automatic discovery of previously unobserved factual knowledge. However, existing MMKGC methods encounter several critical challenges: (i) the imbalance of inter-entity information across different modalities; (ii) the heterogeneity of intra-entity multimodal information; and (iii) for a given entity, the informational contributions of different modalities are inconsistent across contexts. In this paper, we propose a novel L arge model-driven B alanced M ultimodal K nowledge G raph C ompletion framework, termed LBMKGC. Subsequently, to bridge the semantic gap between heterogeneous modalities, LBMKGC aligns the multimodal embeddings of entities semantically by using the CLIP (Contrastive Language-Image Pre-Training) model. Furthermore, LBMKGC adaptively fuses multimodal embeddings with relational guidance by distinguishing between the perceptual and conceptual attributes of triples. Finally, extensive experiments conducted against 21 state-of-the-art baselines demonstrate that LBMKGC achieves superior performance across diverse datasets and scenarios while maintaining efficiency and generalizability. Our code and data are publicly available at: https: //github. com/guoynow/LBMKGC.

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