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.

Human immunodeficiency virus type 1 (HIV-1) is one of the main causative agents of acquired immunodeficiency syndrome (AIDS), and effectively identifying HIV-1 protease cleavage sites (PCSs) is of great importance for the design of new anti-AIDS inhibitors. Computational prediction of HIV-1 PCSs can be used to discover new cleavable substrates, and further facilitates the understanding of substrate specificity. A novel deep learning model, namely DeepHIV, is designed to predict HIV-1 PCSs from substrate sequence information alone. In particular, DeepHIV first applies a convolutional neural network combined with an attention mechanism to capture the rich contextual information of position-specific amino acids in the substrate sequences, thus improving the quality of features learned for substrates. Considering the imbalance observed between cleavable and uncleavable substrates, a biased support vector machine is adopted as the classifier of DeepHIV to complete the prediction task. Experimental results demonstrate that DeepHIV outperforms several state-of-the-art prediction methods across all benchmark datasets and evaluation metrics. Hence, DeepHIV is an accurate and robust tool to predict HIV-1 PCSs. Moreover, the promising predictive performance of DeepHIV also reveals that our deep learning model is capable of fully leveraging the sequence information to effectively learn the latent features of substrates.

To understand the mechanisms of complex systems, attributed graphs (AGs) are recognized as a valuable model by their capability of describing nontrivial topological structures and rich node contents, and their emergence raises new challenges on the task of graph clustering. Although a variety of computational algorithms have been proposed to perform accurate clustering analysis on AGs, most of them are incapable of inferring the cluster labels of nodes through links, thus falling short of explaining node behaviors on how to formulate overlapping clusters. Moreover, the vast amount of links considerably decreases the computation efficiency if they are explicitly taken into account for AG clustering. To overcome this problem, we present a novel variational Bayesian learning model, which avoids generating a complete AG by only simulating the generative process of its skeleton with the prior knowledge on the cluster labels of links. When addressing the inference problem, we develop an efficient algorithm, namely, LCAAG, for determining the optimal cluster labels of nodes by estimating local community structures of links. The convergence of LCAAG has been proved theoretically. Compared with several state-of-the-art algorithms, LCAAG has demonstrated its promising performance in terms of both accuracy and scalability on five different scaled benchmark datasets. The source code and datasets are available at https://github.com/shallowdreamoon/LCAAG.git.

Protein complexes play a crucial role in regulating various biological processes that govern cell activities. Numerous computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. However, many of these algorithms face limitations in effectively leveraging multiview biological information of proteins, restricting their ability to capture the intricate characteristics of protein complexes in PPI networks. While deep learning-based algorithms have significantly advanced the identification of protein complexes, they often integrate graph representation learning techniques into traditional clustering algorithms without explicitly capturing the dependency between protein embeddings and resulting complexes. To address these issues, we present a multiview fusion neural network, named FMvPCI, for protein complex identification via fuzzy clustering. In FMvPCI, we introduce a novel multiview graph convolution encoder to effectively manipulate and fuse the biological information of proteins from different perspectives. Subsequently, the optimization of FMvPCI incorporates our expectations about protein complexes through the concept of fuzzy clustering. This approach unifies the embeddings of proteins and their cluster memberships within a coherent framework. Leveraging a heuristic search strategy, FMvPCI can discover overlapping protein complexes based on the cluster memberships of proteins. A series of experiments on five different PPI networks collected from two species have been conducted to evaluate the performance of FMvPCI by comparing it with state-of-the-art identification algorithms, and the results demonstrate the superior performance of FMvPCI by significantly improving the identification accuracy for protein complexes.

Exploring drug-drug interactions (DDIs) is crucial for avoiding unknown physicochemical incompatibilities between coadministered drugs. While most studies concentrate on detecting the presence or absence of DDIs, they often overlook the diversity of DDI event types that can significantly enhance drug research and guide scientific drug use. To address this limitation, we propose MNCLDDI, a multi-view nested contrastive learning model designed for the precise prediction of DDI events. MN-CLDDI begins by employing a relational graph convolutional network to capture the various explicit relationships between drugs within a multi-relational DDI graph. This is followed by a transformer framework combined with a convolutional neural network (CNN) to learn the biological features of drugs from their Smiles information. The model then integrates these two feature types into a novel multi-view nested contrastive learning framework, thereby improving the expressiveness of drug embeddings from multiple biological perspectives. Experimental results on two real-world datasets demonstrate that MNCLDDI outperforms state-of-the-art models in predicting DDI events. Moreover, our case studies reveal that considering the multi-view features of drugs simultaneously enables MNCLDDI to predict DDI events with greater accuracy and from a more comprehensive perspective, offering valuable insights into the study of DDI events.

Protein complexes are essential in regulating various cellular processes. A number of computational algorithms have been developed to identify protein complexes from protein-protein interaction (PPI) networks, but they are limited in their ability to effectively leverage diverse biological knowledge of proteins. Additionally, while deep learning-based algorithms perform well in identifying protein complexes, they fail to explicitly capture the dependency between protein embeddings and resulting complexes. To address these challenges, this paper proposes a knowledge-guided protein complex identification algorithm with fuzzy-based graph representation learning, named KPCI-FGRL. In particular, a fuzzy-based graph representation learning framework is developed by KPCI-FGRL to manipulate and fuse network structure with multi-view biological knowledge of proteins. During the training phase of KPCI-FGRL, besides employing self-supervised loss to improve the cohesion of the complexes, we also specifically incorporate the expectation about protein complexes based on fuzzy clustering concept, and thus the dependency between protein embeddings and complexes can be coupled. Furthermore, KPCI-FGRL is capable of achieving the identification of overlapping protein complexes through a heuristic search strategy upon fuzzy memberships of proteins. Extensive experimental results on four different PPI networks collected from two species demonstrate that KPCI-FGRL significantly outperforms several state-of-the-art protein complex identification algorithms.

BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .