Abstract Motivation Identifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases. Results In this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA–miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP. 

Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists’ understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Circular RNAs (circRNAs) are specifically and abnormally expressed in disease tissues, and thus can be used as biomarkers to diagnose relevant diseases. Predicting circRNA-disease associations will provide essential clues to reveal molecular mechanisms of disease development and discover novel therapeutic targets. Existing algorithms ignore the heterogeneous biological association information related to microRNAs (miRNAs). Based on a heterogeneous graph embedding model, a novel circRNA-disease association prediction method called HGECDA is developed in this paper. The heterogeneous graph network containing circRNA-miRNA-disease association information is first constructed. To sample the heterogeneous information, the meta-path-based random walk that can capture the relevance between various types of nodes is employed. Then, the path embedding model based on skip-gram and random negative sampling is built to acquire the initial feature vectors of circRNAs and diseases. Finally, the CosMulformer model with linearized self-attention and Hadamard product is designed to obtain the circRNA-disease interaction vectors and conduct the prediction task. Experimental results demonstrate the critical role of miRNA in enriching the information of the feature space, the effectiveness of the CosMulformer model in picking out deep local interaction features, and the feasibility of the Hadamard product chosen as the integration pattern in the CosMulformer model. Compared with existing state-of-the-art methods on the same dataset, HGECDA performs better than the other seven algorithms. Moreover, the case studies about breast cancer and colorectal cancer demonstrate the practical value of HGECDA in predicting potential circRNA-disease associations. 

HGECDA: A Heterogeneous Graph Embedding Model for CircRNA-Disease Association Prediction

A combined deep learning framework for mammalian m6A site prediction

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

The identification of long noncoding RNA (lncRNA)-disease associations is of great value for disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA-disease associations with computational methods. However, the existing methods do not sufficiently extract key features during data processing, and the learning model parts are either less powerful or overly complex. Therefore, there is still potential to achieve better predictive performance by improving these two aspects. In this work, we propose a novel lncRNA-disease association prediction method LDAformer based on topological feature extraction and Transformer encoder. We construct the heterogeneous network by integrating the associations between lncRNAs, diseases and micro RNAs (miRNAs). Intra-class similarities and inter-class associations are presented as the lncRNA-disease-miRNA weighted adjacency matrix to unify semantics. Next, we design a topological feature extraction process to further obtain multi-hop topological pathway features latent in the adjacency matrix. Finally, to capture the interdependencies between heterogeneous pathways, a Transformer encoder based on the global self-attention mechanism is employed to predict lncRNA-disease associations. The efficient feature extraction and the intuitive and powerful learning model lead to ideal performance. The results of computational experiments on two datasets show that our method outperforms the state-of-the-art baseline methods. Additionally, case studies further indicate its capability to discover new associations accurately.

LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder

Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems. The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes explosive synchronization. However, no significant findings have been made on multi-layer complex networks. This paper proposes a frequency-weighted Kuramoto model on a two-layer network and the critical coupling strength of explosive synchronization is obtained by both theoretical analysis and numerical validation. It is found that the critical value is affected by the interaction strength between layers and the number of network oscillators. The explosive synchronization will be hindered by enhancing the interaction and promoted by increasing the number of network oscillators. Our results have importance across a range of engineering and biological research fields.

https://iopscience.iop.org/article/10.1088/1674-1056/28/7/070502/pdf

Explosive synchronization of multi-layer frequency-weighted coupled complex systems

: Using visual analytics techniques to compare genomic data can not only help understand the multi-omics function and structure behind gene sequences and the attributes, but also explore the influence of conservation and variation on genomic genetic relationship deeply. Therefore, visualization approaches are of great significance for data-intensive comparative genomics knowledge discovery. The related research on visual analysis of comparative genomics in recent years are reviewed. First, the data structure and visual analysis tasks of genomics sequences and the derived information are briefly described. Second, the spatial layout of the genome is classified into three categories: vertical discrete, horizontal continuous and radial nested. The advantages and disadvantages of these various mapping methods are also compared and summarized. Third, two main genome comparative relationships are extracted, i.e., interactive association and family hierarchical relationship, and then the relevant suitable visualization approaches are summarized. Finally, the challenges and opportunities of visualization methods for comparative genomics in data fusion, interaction and general framework are pointed out.

A Survey of Visualization Approaches for Comparative Genomics

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LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder

Explosive synchronization of multi-layer frequency-weighted coupled complex systems

A Survey of Visualization Approaches for Comparative Genomics