Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Drug sensitivity is critical for enabling personalized treatment. Many studies have shown that long non-coding RNAs (lncRNAs) are closely related to drug sensitivity because lncRNAs can regulate genes related to drug sensitivity to affect drug efficacy. Exploring lncRNA-drug sensitivity associations has important implications for drug development and disease treatment. However, identifying lncRNA-drug sensitivity associations based on traditional biological approaches is small-scale and time-consuming. In this work, we develop a dual-channel hypergraph neural network-based method named HGNNLDA to infer unknown lncRNA-drug sensitivity associations. To our best knowledge, HGNNLDA is the first computational framework to predict lncRNA-drug sensitivity associations. HGNNLDA applies the hypergraph neural network to obtain high-order neighbor information on the lncRNA hypergraph and the drug hypergraph, respectively, and utilizes a joint update mechanism to generate lncRNA embeddings and drug embeddings. In traditional graphs, an edge contains only two nodes. However, hyperedges in hypergraphs can contain any number of nodes and hypergraphs can well describe the higher-order connectivity of the lncRNA-drug bipartite graphs. The comprehensive experimental results show that HGNNLDA significantly outperforms the other six state-of-the-art models. Case studies on two drugs further illustrate that HGNNLDA is an effective tool to predict lncRNA-drug sensitivity associations.

BACKGROUND: Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. RESULTS: In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. CONCLUSIONS: Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.

Microbes are parasitic in various human body organs and play significant roles in a wide range of diseases. Identifying microbe-disease associations is conducive to the identification of potential drug targets. Considering the high cost and risk of biological experiments, developing computational approaches to explore the relationship between microbes and diseases is an alternative choice. However, most existing methods are based on unreliable or noisy similarity, and the prediction accuracy could be affected. Besides, it is still a great challenge for most previous methods to make predictions for the large-scale dataset. In this work, we develop a multi-component Graph Attention Network (GAT) based framework, termed MGATMDA, for predicting microbe-disease associations. MGATMDA is built on a bipartite graph of microbes and diseases. It contains three essential parts: decomposer, combiner, and predictor. The decomposer first decomposes the edges in the bipartite graph to identify the latent components by node-level attention mechanism. The combiner then recombines these latent components automatically to obtain unified embedding for prediction by component-level attention mechanism. Finally, a fully connected network is used to predict unknown microbes-disease associations. Experimental results showed that our proposed method outperformed eight state-of-the-art methods. Case studies for two common diseases further demonstrated the effectiveness of MGATMDA in predicting potential microbe-disease associations. The codes are available at Github https://github.com/dayunliu/MGATMDA.

Small nucleolar RNAs(snoRNAs) represent an abundant group of noncoding RNAs in the nucleolus of eukaryotes. Recent studies revealed that snoRNAs play a significant role in a wide range of diseases. Identifying snoRNA-disease associations can provide great insights into understanding the disease and treatment and boosting drug discovery development. Traditional methods of using biological experiments are often usually small-scale and time-consuming. Therefore, it is urgent to develop a computational framework to predict snoRNA-disease associations. In this work, we proposed a novel Graph Convolutional Network(GCN) based framework GCNSDA for predicting snoRNA-disease associations. To our best knowledge, GCNSDA is the first framework that uses a graph convolutional network to predict snoRNA-disease associations. GCNSDA is built on the bipartite graph of snoRNAs and diseases; it uses a graph neural network to discover latent factors that cause the association between snoRNAs and diseases and then generate embedded representations of snoRNAs and diseases. Experimental results showed that GCNSDA achieved better performance than the other six state-of-the-art methods. Case study further confirmed the effectiveness of GCNSDA in predicting snoRNA-disease associations.

The research of anticancer drugs has gone through a long process of development, but so far, no drug can cure cancer completely. The drug resistance is one of the main reasons for the failure of cancer treatment. As the relationship between miRNA and cancer is gradually revealed, more and more evidence shows that the sensitivity of cancer cells to anticancer drugs is also affected by miRNA. Research on miRNA-drug sensitivity associations can overcome the challenging clinical situation imposed by drug resistance. However, traditional biological experiments are time-consuming and expensive. Therefore, there is an urgent need to develop a computational method to predict the associations between miRNA and drug sensitivity accurately and efficiently. In this work, we propose a computational method based on simplified GCN to predict the miRNA-drug sensitivity associations, named LGCMDS. We abandon the two common designs in standard GCN-feature transformation and nonlinear activation, only retain the essential component, neighbourhood aggregation, and combine with high-order connectivity in the miRNA-drug graph to effectively integrate the miRNA-drug interactions into the embedding process. The 5-fold cross-validation results show that our proposed method achieves AUC of 0.8872 and AUPR of 0.9026. In comparison with five state-of-the-art models, LGCMDS achieves the best results. In addition, case study for Cisplatin, further proves the effectiveness of LGCMDS in predicting potential miRNA-drug sensitivity associations.

BACKGROUND: Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. RESULTS: In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. CONCLUSION: The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.