Circular RNAs (circRNAs) play a crucial role in gene regulation and have been implicated in the development of drug resistance in cancer, representing a significant challenge in oncological therapeutics. Despite advancements in computational models predicting RNA-drug interactions, existing frameworks often overlook the complex interplay between circRNAs, drug mechanisms, and disease contexts. This study aims to bridge this gap by introducing a novel computational model, circRDRP, that enhances prediction accuracy by integrating disease-specific contexts into the analysis of circRNA-drug interactions. It employs a hybrid graph neural network that combines features from Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) in a two-layer structure, with further enhancement through convolutional neural networks. This approach allows for sophisticated feature extraction from integrated networks of circRNAs, drugs, and diseases. Our results demonstrate that the circRDRP model outperforms existing models in predicting drug resistance, showing significant improvements in accuracy, precision, and recall. Specifically, the model shows robust predictive capability in case studies involving major anticancer drugs such as Cisplatin and Methotrexate, indicating its potential utility in precision medicine. In conclusion, circRDRP offers a powerful tool for understanding and predicting drug resistance mediated by circRNAs, with implications for designing more effective cancer therapies.

In the burgeoning field of RNA research, accurate and efficient RNA sequence representation remains a pivotal challenge, exacerbated by the complexity and diversity of RNA sequences. Addressing the critical need for enhanced sequence representation and the issues of sequence context and structural alignment, this study introduces a novel, comprehensive approach. The proposed model seamlessly integrates sequence masking and subsequence consistency optimization, offering a robust solution to the intricate problem of RNA sequence representation. Utilizing the filtered RNAStralign dataset, encompassing 20,923 sequences, the model's performance is rigorously evaluated employing a Support Vector Machine (SVM) for subsequent RNA family classification tasks. Despite the inherent imbalance in RNA family sequence distribution, the model demonstrates exemplary performance, achieving high classification accuracy and AUPRC values across diverse RNA sequence groups. This balanced and unbiased assessment, ensured by the use of AUPRC as an evaluation metric, highlights the model's practical utility for comprehensive RNA sequence analysis and classification. In essence, this research presents a method for enhanced RNA sequence representation and laying a robust foundation for future advancements in the nuanced field of RNA sequence analysis.

In the field of drug discovery, the accurate prediction of drug-target interactions (DTIs) is a critical yet challenging task, hindered by the intricate dynamics of biological systems and molecular interplay. To address this, we propose the DTI-VGAE model, a novel deep learning framework that integrates variational graph autoencoders (VGAE) with a multi-layer perceptron (MLP) for robust DTI prediction. Our approach focuses on three key aspects: learning distinct representations of drugs and proteins from heterogeneous networks, constructing Drug-Protein Pair (DPP) networks to capture the complex interactions, and employing MLP for the final prediction of DTIs. This comprehensive methodology not only enhances the accuracy of DTI predictions but also ensures greater reliability and stability. Validated through extensive 5-fold cross-validation, the DTI-VGAE model consistently outperforms existing methods, achieving superior average AUROC, AUPR scores, and accuracy. The DTI-VGAE model's innovative integration of VGAE and MLP offers a significant advancement in the computational approach to drug discovery, paving the way for more efficient and precise drug development processes.

Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep learning, have been developed to predict associations between circRNAs and diseases, the biological function of circRNAs has not been fully exploited. Some methods have explored disease-related circRNAs based on different views, but how to efficiently use the multi-view data about circRNA is still not well studied. Therefore, we propose a computational model to predict potential circRNA-disease associations based on collaborative learning with circRNA multi-view functional annotations. First, we extract circRNA multi-view functional annotations and build circRNA association networks, respectively, to enable effective network fusion. Then, a collaborative deep learning framework for multi-view information is designed to get circRNA multi-source information features, which can make full use of the internal relationship among circRNA multi-view information. We build a network consisting of circRNAs and diseases by their functional similarity and extract the consistency description information of circRNAs and diseases. Last, we predict potential associations between circRNAs and diseases based on graph auto encoder. Our computational model has better performance in predicting candidate disease-related circRNAs than the existing ones. Furthermore, it shows the high practicability of the method that we use several common diseases as case studies to find some unknown circRNAs related to them. The experiments show that CLCDA can efficiently predict disease-related circRNAs and are helpful for the diagnosis and treatment of human disease.

Circular RNAs (circRNAs) have important effects on various biological processes, and their dysfunction is closely related to the emergence and development of diseases. Identifying the associations between circRNAs and diseases is helpful in analyzing the pathogenesis of diseases. Therefore, it is necessary to develop effective computational methods for predicting circRNA-disease associations. Here, we present a computational model called HRCDA to predict associations between circRNA and disease based on heterogeneous graph representation. Firstly, an integrated network of circRNA functional similarity is built by Random Walk with Restart in the view of biological functions of circRNA. Then, a heterogeneous graph of circRNAs and diseases is constructed with known circRNA-disease associations. Finally, we design a heterogeneous graph representation learn model based on Graph Auto-Encoder (GAE) to predict circRNA-disease associations. Experiments have shown that the proposed method perform better than existing state-of-the-art methods and can be an effective tool to predict potential disease-related circRNAs.