Circular RNAs (circRNAs) play a critical role in gene regulation and association with diseases due to their specialized structure, which is formed as a closed loop structure during a non-canonical splicing process where the donor site back-spliced to an upstream acceptor site. As fundamental work to clarify their functions and mechanisms, a large number of computational methods for predicting circRNA formation have been proposed, among which, in particular, deep learning is utilized to capture relevant patterns from raw RNA sequences and model their interactions to facilitate prediction. However, these methods fail to fully utilize the important characteristics of back-splicing events, i.e., the positional information of the splice sites and the interaction features of its flanking sequences, for prediction. To this end, we hereby propose a novel approach called SIDE for predicting circRNA back-splicing events using only nucleotide sequences. Our model employs a dual encoder to capture global and interactive features of the sequence, and then a decoder designed by the contrastive learning to fuse out discriminative features improving the prediction of circRNAs formation. Empirical results on three real-world datasets have shown the effectiveness of SIDE. Our code is publicly available at https://github.com/scu-kdde/Bioinfo-SIDE-2023.

Predicting RNA-protein binding sites helps to explore the mechanisms of the interaction between RNA and proteins. Numerous deep learning methods have been applied to predict RNA-protein binding sites. Some of these methods use only sequence information for prediction which could lose information about the topology. And there may be a loss of important information if the secondary structure features are simply represented as one-hot matrices. Furthermore, existing deep learning methods are usually based on convolutional neural networks for feature extraction, which tend to focus on local features. As for the information of the whole sequence, existing methods usually ignore global features. Therefore, we propose a novel deep learning model called DARE for RNA-protein binding sites prediction using both sequence and secondary structure information of RNA. DARE employs the secondary structure feature extraction module to capture the features of the RNA secondary structure and learn the topological information. Therefore, we design a local feature extraction module and a global feature integration module to capture the whole information of RNA. Thus we can achieve the purpose of complementary information. Extensive experiments demonstrate that DARE outperforms baselines. Our analysis of the case study further confirm the effectiveness of DARE.

Drug-target interaction (DTI) prediction is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising strategy. However, there are few methods which explore solving the cold-start problem in DTI prediction scenarios due to most of existing methods require modeling under the existing interaction that can’t effectively capture information from new drugs and new targets which have few interactions in existing literature. In this paper, we propose a graph transformer method based on meta-learning named MGDTI to fill the gap. In particular, we employ drug-drug similarity and target-target similarity as additional information for network to mitigate the scarcity of interactions. Besides, we trained our model via meta-learning to be adaptive to cold-start tasks. Moreover, we introduced graph transformer to prevent over-smoothing by capturing long-range dependencies. Comparison results on the benchmark dataset demonstrate that our proposed MGDTI is effective in the DTI prediction.

Gene similar aspects provide reliable explanation in understanding the biological roles and gene functions. As the volume of biomedical data expands, most of the current methods for similar explanation among genes are no longer applicable. Limited by information sources and search effciency, these methods cannot be flexible and effcient for the similarity analysis. We hereby propose a flexible method VENUS to analyze gene similar aspect among multiple genes on heterogeneous information networks, which constructed from public biomedicine databases and literature. VENUS infers the semantic and structural similarity of the query genes by gene community search. In this way, VENUS narrows the search space when searching information network within an acceptable time cost. Besides, VENUS is not limited by inherent domain knowledge and is adaptive to large-scale networks. Through experiments on multiple different public data sources, it demonstrates that VENUS is effective and effcient.

Exploring disease-associated human cells can help researchers identify pathogenesis and develop treatments. However, mining the relationships between diseases and human cells is difficult due to several challenges, including information extraction from unstructured knowledge sources. Biomedicine literature on diseases and cells contains a wealth of potentially useful information but is underutilized due to the complexity of unstructured information. A knowledge graph provides organized and structured relationships among entities and can be served as a solution. Nevertheless, few existing tools can be found for constructing a disease-aware human cell knowledge graph from biomedicine literature. To fill this gap, we propose a novel approach, called DEAL, to construct a disease-aware human cell knowledge graph from biomedicine literature. Technically, DEAL builds the knowledge graph through a series of steps including the processing of distant supervision data, the training of a distant supervision relation extraction model, the extraction of triples, and the knowledge graph construction. Experiments demonstrate that DEAL can effectively express the extracted entities related to diseases and cells and their relationships.

Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive 1earning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.