Drug-drug interaction (DDI) refers to the interaction relationships between drugs. Discovering new DDIs is crucial for advancing drug development and enhancing clinical treatments. Given the significant progress achieved through graph neural networks (GNNs), network-based models have become a prevalent approach for tackling this challenge. However, current network-based approaches are incapable of seamlessly integrating a wide range of information. Motivated by this discovery, we propose a novel model, namely LLM-DDI, which aims to comprehensively tackle DDI prediction tasks by integrating various information of molecules in the BKG. LLM-DDI initially incorporates the generative pre-trained transformer (GPT) model to generate embeddings for each molecule within the biomedical knowledge graph (BKG). These embeddings encompass diverse types of information pertaining to each molecule. Subsequently, LLM-DDI utilizes a message-passing GNN framework to enhance the learning of molecular representations with the embeddings derived from GPT as input. LLM-DDI governs the propagation of information within the BKG by semantic relationships. These semantic relationships determine how information flows and is exchanged between different entities in the BKG. Finally, LLM-DDI leverages the learned drug representations to predict potential DDIs. Experiments show the effectiveness of LLM-DDI, as it achieves the best performance on two real-world datasets, providing valuable guidance for drug development and clinical treatment.

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse effects and compromised therapeutic efficacy. Accurate prediction of DDI events, which involve not only identifying interacting drug pairs but also characterizing the specific nature and context of their interactions, is essential for drug safety and personalized medicine. In this study, we propose a novel Multi-view Contrastive Learning framework, namely MCL-DDI, for DDI Event Prediction by leveraging multi-view representations of drugs to enhance predictive performance. MCL-DDI integrates molecular structures and network features, capturing complementary information about drug properties and interactions. By employing contrastive learning, we align and unify drug representations across these diverse views, enabling the framework to distinguish complex interaction patterns. Extensive experiments on benchmark datasets demonstrate that MCL-DDI outperforms state-of-the-art methods in terms of predictive accuracy. Furthermore, case studies highlight the model's ability to identify clinically relevant DDIs, offering practical insights for drug development and risk assessment. Our work establishes a robust and accurate paradigm for DDI event prediction, paving the way for safer and more effective pharmacological interventions.

Accurately identifying cancer driver genes is crucial for understanding tumorigenesis and advancing precision oncology. However, integrating multi-omics data within complex biological networks remains challenging, particularly when it comes to capturing diverse structural information and leveraging the distinct signals from different omics modalities. While graphbased methods have demonstrated high accuracy in cancer gene identification, they might overlook the heterogeneity between omics features. To address this limitation, we propose CGI-MoE, a Mixture-of-Experts-inspired graph representation learning framework that incorporates omics-feature-specific expert modules based on graph transformers, together with adaptive gating and subgraph aggregation mechanisms. CGI-MoE extracts both local and global structural encodings for each node by sampling multiple subgraphs, enabling the model to capture comprehensive and robust network features. Each expert module focuses on a specific omics modality, and their outputs are fused by a lightweight gating network that dynamically weighs their contributions. When evaluated on both homogeneous and heterogeneous benchmark datasets, CGI-MoE achieves state-of-the-art performance in terms of accuracy, AUC, and AUPR, consistently surpassing existing methods. Ablation studies further highlight the critical roles of each component in achieving robust performance. Using the trained models, CGI-MoE predicted 46 novel cancer gene candidates from all unlabeled genes, demonstrating its potential for novel discovery and for deepening our understanding of cancer development. The code is available at https://github.com/moomight/CGI-MoE.

Drug-drug interactions (DDIs) can critically affect treatment safety and efficacy, especially when multiple drugs are prescribed concurrently. Such interactions may alter pharmacological activity and complicate therapeutic outcomes. Although graph-based learning methods have advanced DDI prediction, most rely solely on drug-drug networks, overlooking valuable information from auxiliary drug-centric networks. To overcome this limitation, it is crucial to adopt more hierarchical strategies that incorporate both drug-drug and auxiliary drug-centric networks to capture nuanced drug representations. In this research, we construct a hierarchical network to incorporate both drug-drug and auxiliary networks as distinct layers and propose a drug-centric hierarchical augmentation method (DCHA) for DDI prediction. DCHA encompasses three core components: a hierarchical learner, a layer discriminator, and a DDI predictor. The hierarchical learner employs a fusion gate to compute augmented drug representations by integrating core drug representations from the drug-drug network and auxiliary representations from other auxiliary drug-centric networks. The layer discriminator helps the hierarchical learner in capturing auxiliary drug representations. With the help of the hierarchical learner and layer discriminator, the DDI predictor finally augments the performance of DDI prediction. Extensive experimentation demonstrates that DCHA outperforms existing state-of-the-art methods in DDI prediction.

As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive results, it often fails to account for false DDI in the constructed DDI networks. Such inaccuracies caused by data errors, false alarms, or incorrect drug details can skew the network's structure and hinder the accuracy of GNN-based predictions. To tackle this challenge, we propose ANSM, a network-enhancement method specifically designed to identify and attenuate spurious links between drugs for ensuring the accuracy of DDI networks. ANSM integrates three key components: the feature extractor, the network optimizer, and the discriminative classifier. The feature extractor captures local structural features from drug node pairs, while the network optimizer leverages network information to improve feature extraction and reduce the impact of spurious DDI links. The discriminative classifier then identifies potential spurious links. Experimental results demonstrate that ANSM outperforms state-of-the-art methods in identifying spurious DDI.

With the growing popularity of artificial intelligence in drug discovery, many deep-learning technologies have been used to automatically predict unknown drug-target interactions (DTIs). A unique challenge in using these technologies to predict DTI is fully exploiting the knowledge diversity across different interaction types, such as drug-drug, drug-target, drug-enzyme, drug-path, and drug-structure types. Unfortunately, existing methods tend to learn the specifical knowledge on each interaction type and they usually ignore the knowledge diversity across different interaction types. Therefore, we propose a multitype perception method (MPM) for DTI prediction by exploiting knowledge diversity across different link types. The method consists of two main components: a type perceptor and a multitype predictor. The type perceptor learns distinguished edge representations by retaining the specifical features across different interaction types; this maximizes the prediction performance for each interaction type. The multitype predictor calculates the type similarity between the type perceptor and predicted interactions, and the domain gate module is reconstructed to assign an adaptive weight to each type perceptor. Extensive experiments demonstrate that our proposed MPM outperforms the state-of-the-art methods in DTI prediction.

Heterogeneous social networks, which are characterized by diverse interaction types, have resulted in new challenges for missing link prediction. Most deep learning models tend to capture type-specific features to maximize the prediction performances on specific link types. However, the types of missing links are uncertain in heterogeneous social networks; this restricts the prediction performances of existing deep learning models. To address this issue, we propose a multi-type transferable method (MTTM) for missing link prediction in heterogeneous social networks, which exploits adversarial neural networks to remain robust against type differences. It comprises a generative predictor and a discriminative classifier. The generative predictor can extract link representations and predict whether the unobserved link is a missing link. To generalize well for different link types to improve the prediction performance, it attempts to deceive the discriminative classifier by learning transferable feature representations among link types. In order not to be deceived, the discriminative classifier attempts to accurately distinguish link types, which indirectly helps the generative predictor judge whether the learned feature representations are transferable among link types. Finally, the integratedMTTMis constructed on this minimax two-player game between the generative predictor and discriminative classifier to predict missing links based on transferable feature representations among link types. Extensive experiments show that the proposedMTTMcan outperform state-of-the-art baselines for missing link prediction in heterogeneous social networks.

Despite the massive surge of evolving social network analysis in popularity, existing research usually represent the observed social interactions among individuals as completely credible edges. However, due to information inaccuracy, individual non-response and dropout, and sampling biases in observations, the evolving noisy social network that coexists true edges and spurious edges is pervasive in actual applications, where the ignoration of credibility otherness of observed edges could lead to the wrong estimates of social properties and misleading conclusions. To discover credible edge information to shape correct social interactions among individuals, we propose a universal and explainable multiple-neighbor evolutional filtering method (MEFM) to evaluate how credible of observed edges to ‘truly’ exist in the evolving noisy social network.MEFMconsists of an evolutional extractor and a filtering evaluator. To resist the noisy disturbance, the evolutional extractor exploits the evolutional states of edges from the perspective of evolution mechanisms within multiple-neighbor ranges, which applies different link prediction algorithms to fit the evolution mechanism in the formation of each edge. Further, the filtering evaluator reconstructs Kalman filter to predict and refine the evolutional states of edges based on their evolving local structures. As a result,MEFMcombines the evolutional extractor and the filtering evaluator to analyze the evolutional fluctuations of the observed edges to evaluate their credibility. Extensive experiments on real-world datasets demonstrate that our proposedMEFMcan effectively and reasonably evaluate edge credibility in evolving noisy social networks.

As news has become an important way to obtain in-formation, the spread of fake news has caused serious social problems, such as misleading readers and damaging the authority of the government. Therefore, fake news detection has become an important field in social network research. One challenge of fake news detection is how to explore the common latent semantics, which are universally implied in fake news. However, the existing methods are not enough for mining this kind of semantic information. Therefore, we proposed a fake news detection framework named Common Latent Semantics Matching Model (CLSMM), which improves the performance of fake news detection by utilizing common latent semantics in fake news. First, we use BERT model to extract common latent semantics of fake news and use summary generation model to extract distinct latent semantics among each piece of news. Second, we rank the semantic credibility score according to the matching degree of the two kinds of latent semantics mentioned above. Finally, these semantic credibility scores are injected into a fake news classifier to improve the detection performance. Experiments are based on two large scale real-world social media datasets, namely Liar and BuzzFeed. The experimental results show that our model can outperform the accuracy of the state-of-the-art methods by 2.7% and 17.26% on Liar and BuzzFeed, respectively.