Circular RNAs (circRNAs) represent a distinctive class of non-coding RNAs with covalently closed loop structures that play crucial regulatory roles in drug response. While existing computational methods have achieved certain progress in prediction tasks, they primarily relied on circRNA genotypes and traditional molecular fingerprints, with limited utilization of multi-omics data and inadequate consideration of heterogeneous network topology. To address these limitations, this study proposed the CircRNA-Drug Bipartite Graph Transformer (CDBGT) framework to predict associations. Rather than limiting to associations between circRNA genotypes and drugs, this study integrated circRNA-drug response and target association information from multiple databases. CDBGT employed pre-trained models RNA-FM and ChemBERTa to extract features of sequence and molecular fingerprint and utilized multi-omics data to construct similarity matrices. The framework incorporated a bipartite graph transformer with topological positional encoding, comprehensively considering degree encoding, degree ranking encoding and spectral encoding to extract topological information from heterogeneous networks. Experimental results showed that CDBGT performed stably in 5-fold cross-validation. On the Response dataset, it achieved ROC-AUC of 0.9674 and PR-AUC of 0.9540, while on the Target dataset it reached ROC-AUC of 0.8621. Compared with existing methods, it showed an improvement of 3.20 to 26.87 percentage points in ROC-AUC. Ablation experiments demonstrated the necessity of each module. Through literature-supported case studies, this work suggested potential directions for circRNA-based therapeutic research.

PIWI-interacting RNAs (piRNAs) are key regulators in multiple disease processes, yet predicting their associations with diseases remains difficult due to data sparsity and feature complexity. To address this challenge, a multimodal deep learning framework, PDFBG, was proposed to formulate piRNA-disease association prediction as a three-class classification problem (positive, negative, and unrelated). PDFBG constructed a network using the secondary structure of piRNA and employed the BERT language model to generate feature embeddings for each nucleotide. These embed dings are then fused with topologi-cal structure information via a Graph Convolutional Network (GCN). In terms of diseases, this study constructed disease fea-ture embeddings based on MeSH semantic similarity, Gaussian interaction profile (GIP) kernel and disease-gene associations. Finally, an MLP classifier was used for end - to-end prediction. Experiments on benchmark datasets shown that PDFBG outper-forms state-of-the-art methods, achieving an accuracy of 0.9649 and AUC of 0.9761. Ablation studies confirmed the critical role of RNA-BERT and structural fusion. This work provided a robust and interpretable tool for exploring complex piRNA- disease relationships, while establishing a new paradigm for piRNA research. The code and dataset can be obtained from https://github.com/He-EugeneIPDFBG.

Drug combination synergy is crucial in pharmacology, as it can enhance disease treatment efficacy or reduce drug resistance when administered in combination. Accurate prediction of drug combination synergy is vital for optimizing therapeutic regimens and improving treatment effectiveness. However, existing computational methods primarily rely on drug sequence and structural features, making them difficult to capture complex network relationships and global information-especially lacking the ability to perform cross-modal fusion. In this study, we proposed a method called SDCGDCP for predicting drug combination synergy. It processed drug molecular structures (graph structures and molecular fingerprints), target biological activity information and integrated cell line whole-genome expression profiles to construct multi-level combined node representations. A drug-cell line pair graph was accordingly generated. SDCGDCP updated node and edge representations via a GatedGCN module and derived five types of structural encodings (centrality, spatial, edge, Laplacian positional and node-similarity encodings), after which the graph language model Graphormer is employed to capture long-range node interactions. Finally, drug combination synergy was predicted using an MLP and SoftMax classifier. Extensive evaluations showed that SDCGDCP outperforms other state-of-the-art methods on the DrugCombDB dataset, achieving an AUROC of 0.923 and AUPRC of 0.885. Ablation experiments validated the effectiveness of each feature and encoding module. Meanwhile, we conducted case analyses on the predicted drug combination synergies. The results were supported by evidence from several pharmaceutical studies. This highlights potential of SDCGDCP in enhancing drug synergy prediction and optimizing combination therapies. The source code and data for SDCGDCP are available at https://github.com/Philosopher-Zhao/SDCGDCP.

It is well-established that miRNAs play a crucial role in the occurrence and development of diseases. Current miRNA-disease associations prediction research faces several challenges, including model bias due to data sparsity, information loss from overlooked complex relationships during feature fusion and insufficient capability of existing methods to capture the intricate relationships (between miRNAs, genes, lncRNAs and diseases), thereby limiting prediction accuracy. Based on hybrid gated GNN and multi-data fusion, a method (PMDGGM) for predicting miRNA-disease associations is proposed in this study. PMDGGM constructed seven similarity networks by comprehensively considering the relationships between miRNA and related genes, miRNA, lncRNA and diseases. It provides a solid foundation for feature fusion and information propagation. Subsequently, the method captures the complex relationships between heterogeneous nodes through a bilinear pooling layer and uses a gating mechanism to fuse multi-source heterogeneous features, thereby predicting miRNA-disease associations more accurately. The experimental results show that the method performs well and has significant advantages in predicting the miRNA-disease associations. Among various evaluation metrics, especially the AUCs of ROC and PR curves, the performance of method is outstanding, reaching a high level of 0.9413 and 0.9362. The study conducted case analyses on two diseases heart failure and acute myeloid leukemia. The predicted associated miRNAs can be validated by existing biomedical research efforts. The source code and data of PMDGGM can be publicly accessed on GitHub for further research and verification: https://github.com/WangYeQianger/PMDGGM

Due to the complexity and diversity of modern diseases, the combination of drugs has become the first choice. According to the graph theory in graph theory, we transform the original link prediction problem into the node identification problem by establishing the graph of drug side effect network - polypharmacy network to predict. A combined drug side effect prediction model (CDSG) was constructed on the polypharmacy network graph, based on graph attention mechanism and Graph Sample and Aggregate (GraphSAGE). Firstly, the side-effect network was constructed by using the side-effect relationship between drugs and drugs, and then the polypharmacy network was constructed. Then the target gene of the drug is encoded and the feature vector of the drug is established. Furthermore, the advanced feature representations of drugs are learned by utilizing the graph attention network and the GraphSAGE algorithm. Finally, the advanced drug characteristics were connected to the fully connected layer for classification prediction. On a baseline dataset of 1138 side effect types of 257 drugs, we conducted different methods of feature fusion experiment, ablation experiment, 5-fold crossover experiment, and compared CDSG with several computing models such as traditional GCN model, the GAT model and matrix decomposition models, and the final model achieved good results.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Combining large-scale biological data, using computational methods to mine potential disease-gene associations is a popular strategy. At the same time, bio-inspired intelligent optimization has always been a hot research field of intelligent computing. In this study, we apply the pigeon-inspired optimization (PIO) algorithm to the identification of human disease-genes. The problem of predicting disease-genes is translated into a single-objective optimization problem. A reasonable objective function is designed to measure the association between genes and inquiring diseases in a heterogeneous network, and the corresponding probability matrix is generated. The experimental results show that the proposed method (PDG-PIO) can accurately identify disease-genes.

Identification of protein complexes is very important to investigate the characteristics of biological processes. Most of existing protein complex clustering algorithms were often run only on a static protein-protein interaction (PPI) network. The dynamic characteristics of interactions were ignored. In order to solve the problem, a new clustering algorithm (TP-WDPIN) was proposed which is based on the concept of topological potential to measure the importance of proteins in the process of detecting seed proteins and then to mine protein complexes from weighted dynamic PPI network. The algorithm used features of core-attachment of complexes and split low density cores to improve density of cores for achieving better clustering results. Experiment results showed that the proposed TP-WDPIN algorithm has better performance than other algorithms on two PPI databases.