Potential miRNA-disease Associations Research Articles

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.

Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. Therefore, many computational methods have been developed to identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment. In this study, we also introduced a new computational method called PMDAGS, which predicts miRNA-disease associations by utilizing graph nonlinear diffusion convolution network and similarities. PMDAGS first calculates miRNA similarity and disease similarity based on miRNA-target interactions, disease-gene associations and known miRNA-disease associations, respectively. Next, we construct the initial feature of each miRNA (disease) by concatenating its final similarity vector with its known association vector. Based on the known miRNA-disease association network and the initial feature vector of each node, we further apply nonlinear diffusion graph convolution network model to extract the feature embedding vectors. Finally, we concatenate the feature embedding vectors of miRNA and disease and input them into a multi-layer perceptron to identify potential miRNA-disease associations. We conduct 5-fold cross validation (5CV), 10-fold cross validation (10CV), and global leave-one-out cross validation (GLOOCV) on HMDD v2.0 and HMDD v3.2. PMDAGS achieves AUCs of 0.9222, 0.9228, and 0.9221 under 5CV, 10CV and GLOOCV on HMDD v2.0, respectively. In addition, PMDAGS also achieves AUC values of 0.9366, 0.9377, and 0.9376 under 5CV, 10CV and GLOOCV on HMDD v3.2, respectively. According to the experimental results, we can conclude that PMDAGS outperforms other compared methods and can effectively predict miRNA-disease associations.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction.

MicroRNAs (miRNAs) synergize with various biomolecules in human cells resulting in diverse functions in regulating a wide range of biological processes. Predicting potential disease-associated miRNAs as valuable biomarkers contributes to the treatment of human diseases. However, few previous methods take a holistic perspective and only concentrate on isolated miRNA and disease objects, thereby ignoring that human cells are responsible for multiple relationships. In this work, we first constructed a multi-view graph based on the relationships between miRNAs and various biomolecules, and then utilized graph attention neural network to learn the graph topology features of miRNAs and diseases for each view. Next, we added an attention mechanism again, and developed a multi-scale feature fusion module, aiming to determine the optimal fusion results for the multi-view topology features of miRNAs and diseases. In addition, the prior attribute knowledge of miRNAs and diseases was simultaneously added to achieve better prediction results and solve the cold start problem. Finally, the learned miRNA and disease representations were then concatenated and fed into a multi-layer perceptron for end-to-end training and predicting potential miRNA-disease associations. To assess the efficacy of our model (called MUSCLE), we performed 5- and 10-fold cross-validation (CV), which got average the Area under ROC curves of 0.966${\pm }$0.0102 and 0.973${\pm }$0.0135, respectively, outperforming most current state-of-the-art models. We then examined the impact of crucial parameters on prediction performance and performed ablation experiments on the feature combination and model architecture. Furthermore, the case studies about colon cancer, lung cancer and breast cancer also fully demonstrate the good inductive capability of MUSCLE. Our data and code are free available at a public GitHub repository: https://github.com/zht-code/MUSCLE.git.

SGLMDA: A Subgraph Learning-based Method for miRNA-disease Association Prediction.

MicroRNAs (miRNA) are endogenous non-coding RNAs, typically around 23 nucleotides in length. Many miRNAs have been founded to play crucial roles in gene regulation though post-transcriptional repression in animals. Existing studies suggest that the dysregulation of miRNA is closely associated with many human diseases. Discovering novel associations between miRNAs and diseases is essential for advancing our understanding of disease pathogenesis at molecular level. However, experimental validation is time-consuming and expensive. To address this challenge, numerous computational methods have been proposed for predicting miRNA-disease associations. Unfortunately, most existing methods face difficulties when applied to large-scale miRNA-disease complex networks. In this paper, we present a novel subgraph learning method named SGLMDA for predicting miRNA-disease associations. For miRNA-disease pairs, SGLMDA samples K-hop subgraphs from the global heterogeneous miRNA-disease graph. It then introduces a novel subgraph representation algorithm based on Graph Neural Network (GNN) for feature extraction and prediction. Extensive experiments conducted on benchmark datasets demonstrate that SGLMDA can effectively and robustly predict potential miRNA-disease associations. Compared to other state-of-the-art methods, SGLMDA achieves superior prediction performance in terms of Area Under the Curve (AUC) and Average Precision (AP) values during 5-fold Cross-Validation (5CV) on benchmark datasets such as HMDD v2.0 and HMDD v3.2. Additionally, case studies on Colon Neoplasms and Triple-Negative Breast Cancer (TNBC) further underscore the predictive power of SGLMDA. The dataset and source code of SGLMDA are available at https://github.com/cunmeiji/SGLMDA.

DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation

BackgroundMiRNAs are involved in the occurrence and development of many diseases. Extensive literature studies have demonstrated that miRNA-disease associations are stratified and encompass ~ 20% causal associations. Computational models that predict causal miRNA-disease associations provide effective guidance in identifying novel interpretations of disease mechanisms and potential therapeutic targets. Although several predictive models for miRNA-disease associations exist, it is still challenging to discriminate causal miRNA-disease associations from non-causal ones. Hence, there is a pressing need to develop an efficient prediction model for causal miRNA-disease association prediction.ResultsWe developed DNI-MDCAP, an improved computational model that incorporated additional miRNA similarity metrics, deep graph embedding learning-based network imputation and semi-supervised learning framework. Through extensive predictive performance evaluation, including tenfold cross-validation and independent test, DNI-MDCAP showed excellent performance in identifying causal miRNA-disease associations, achieving an area under the receiver operating characteristic curve (AUROC) of 0.896 and 0.889, respectively. Regarding the challenge of discriminating causal miRNA-disease associations from non-causal ones, DNI-MDCAP exhibited superior predictive performance compared to existing models MDCAP and LE-MDCAP, reaching an AUROC of 0.870. Wilcoxon test also indicated significantly higher prediction scores for causal associations than for non-causal ones. Finally, the potential causal miRNA-disease associations predicted by DNI-MDCAP, exemplified by diabetic nephropathies and hsa-miR-193a, have been validated by recently published literature, further supporting the reliability of the prediction model.ConclusionsDNI-MDCAP is a dedicated tool to specifically distinguish causal miRNA-disease associations with substantially improved accuracy. DNI-MDCAP is freely accessible at http://www.rnanut.net/DNIMDCAP/.

MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA.

Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion

miRNAs are a class of small non-coding RNA molecules that play important roles in gene regulation. They are crucial for maintaining normal cellular functions, and dysregulation or dysfunction of miRNAs which are linked to the onset and advancement of multiple human diseases. Research on miRNAs has unveiled novel avenues in the realm of the diagnosis, treatment, and prevention of human diseases. However, clinical trials pose challenges and drawbacks, such as complexity and time-consuming processes, which create obstacles for many researchers. Graph Attention Network (GAT) has shown excellent performance in handling graph-structured data for tasks such as link prediction. Some studies have successfully applied GAT to miRNA-disease association prediction. However, there are several drawbacks to existing methods. Firstly, most of the previous models rely solely on concatenation operations to merge features of miRNAs and diseases, which results in the deprivation of significant modality-specific information and even the inclusion of redundant information. Secondly, as the number of layers in GAT increases, there is a possibility of excessive smoothing in the feature extraction process, which significantly affects the prediction accuracy. To address these issues and effectively complete miRNA disease prediction tasks, we propose an innovative model called Multiplex Adaptive Modality Fusion Graph Attention Network (MAMFGAT). MAMFGAT utilizes GAT as the main structure for feature aggregation and incorporates a multi-modal adaptive fusion module to extract features from three interconnected networks: the miRNA-disease association network, the miRNA similarity network, and the disease similarity network. It employs adaptive learning and cross-modality contrastive learning to fuse more effective miRNA and disease feature embeddings as well as incorporates multi-modal residual feature fusion to tackle the problem of excessive feature smoothing in GATs. Finally, we employ a Multi-Layer Perceptron (MLP) model that takes the embeddings of miRNA and disease features as input to anticipate the presence of potential miRNA-disease associations. Extensive experimental results provide evidence of the superior performance of MAMFGAT in comparison to other state-of-the-art methods. To validate the significance of various modalities and assess the efficacy of the designed modules, we performed an ablation analysis. Furthermore, MAMFGAT shows outstanding performance in three cancer case studies, indicating that it is a reliable method for studying the association between miRNA and diseases. The implementation of MAMFGAT can be accessed at the following GitHub repository: https://github.com/zixiaojin66/MAMFGAT-master.

Prediction of miRNA–disease associations based on strengthened hypergraph convolutional autoencoder

Most existing graph neural network-based methods for predicting miRNA–disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA–disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.

DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction.

A growing number of studies have shown that aberrant microRNA (miRNA) expression is closely associated with the evolution and development of various complex human diseases. These key biomarkers' identification and observation are significant for gaining a deeper understanding of disease pathogenesis and therapeutic mechanisms. Consequently, pinpointing potential miRNA-disease associations (MDA) has become a prominent bioinformatics subject, encouraging several new computational methods given the advances in graph neural networks (GNN). Nevertheless, these existing methods commonly fail to exploit the network nodes' global feature information, leaving the generation of high-quality embedding representations using graph properties as a critical unsolved issue. Addressing these challenges, we introduce the DAEMDA, a computational method designed to optimize the current models' efficacy. First, we construct similarity and heterogeneous networks involving miRNAs and diseases, relying on experimentally corroborated miRNA-disease association data and analogous information. Then, a newly-fashioned parallel dual-channel feature encoder, designed to better comprehend the global information within the heterogeneous network and generate varying embedding representations, follows this. Ultimately, employing a neural network classifier, we merge the dual-channel embedding representations and undertake association predictions between miRNA and disease nodes. The experimental results of five-fold cross-validation and case studies of major diseases based on the HMDD v3.2 database show that this method can generate high-quality embedded representations and effectively improve the accuracy of MDA prediction.

MSHGANMDA: Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction.

MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGANMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGANMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNA-disease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGANMDA in predicting unknown diseases.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases’ biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

Improving the identification of miRNA-disease associations with multi-task learning on gene-disease networks.

MicroRNAs (miRNAs) are a family of non-coding RNA molecules with vital roles in regulating gene expression. Although researchers have recognized the importance of miRNAs in the development of human diseases, it is very resource-consuming to use experimental methods for identifying which dysregulated miRNA is associated with a specific disease. To reduce the cost of human effort, a growing body of studies has leveraged computational methods for predicting the potential miRNA-disease associations. However, the extant computational methods usually ignore the crucial mediating role of genes and suffer from the data sparsity problem. To address this limitation, we introduce the multi-task learning technique and develop a new model called MTLMDA (Multi-Task Learning model for predicting potential MicroRNA-Disease Associations). Different from existing models that only learn from the miRNA-disease network, our MTLMDA model exploits both miRNA-disease and gene-disease networks for improving the identification of miRNA-disease associations. To evaluate model performance, we compare our model with competitive baselines on a real-world dataset of experimentally supported miRNA-disease associations. Empirical results show that our model performs best using various performance metrics. We also examine the effectiveness of model components via ablation study and further showcase the predictive power of our model for six types of common cancers. The data and source code are available from https://github.com/qwslle/MTLMDA.

KATZNCP: a miRNA–disease association prediction model integrating KATZ algorithm and network consistency projection

BackgroundClinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA–disease associations predicted by computational methods are the best complement to biological experiments.ResultsIn this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA–disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA–disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA–disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.ConclusionA new computational model KATZNCP was proposed for predicting potential miRNA–drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA–disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Prediction of miRNA-disease associations by neural network-based deep matrix factorization.

MicroRNA(miRNA) is a class of short non-coding RNAs with a length of about 22 nucleotides, which participates in various biological processes of cells. A number of studies have shown that miRNAs are closely related to the occurrence of cancer and various human diseases. Therefore, studying miRNA-disease associations is helpful to understand the pathogenesis of diseases as well as the prevention, diagnosis, treatment and prognosis of diseases. Traditional biological experimental methods for studying miRNA-disease associations have disadvantages such as expensive equipment, time-consuming and labor-intensive. With the rapid development of bioinformatics, more and more researchers are committed to developing effective computational methods to predict miRNA-disease associations in roder to reduce the time and money cost of experiments. In this study, we proposed a neural network-based deep matrix factorization method named NNDMF to predict miRNA-disease associations. To address the problem that traditional matrix factorization methods can only extract linear features, NNDMF used neural network to perform deep matrix factorization to extract nonlinear features, which makes up for the shortcomings of traditional matrix factorization methods. We compared NNDMF with four previous classical prediction models (IMCMDA, GRMDA, SACMDA and ICFMDA) in global LOOCV and local LOOCV, respectively. The AUCs achieved by NNDMF in two cross-validation methods were 0.9340 and 0.8763, respectively. Furthermore, we conducted case studies on three important human diseases (lymphoma, colorectal cancer and lung cancer) to validate the effectiveness of NNDMF. In conclusion, NNDMF could effectively predict the potential miRNA-disease associations.

AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA–disease associations identification

In recent years, many experiments have proved that microRNAs (miRNAs) play a variety of important regulatory roles in cells, and their abnormal expression can lead to the emergence of specific diseases. Therefore, it is greatly valuable to do research on the association between miRNAs and diseases, which can effectively help prevent and treat miRNA-related diseases. At present, effective computational methods still need to be developed to better identify potential miRNA-disease associations. Inspired by graph convolutional networks, in this study, we propose a new method based on Attention aware Multi-view similarity networks and Hypergraph learning for MiRNA-Disease Associations identification (AMHMDA). First, we construct multiple similarity networks for miRNAs and diseases, and exploit the graph convolutional networks fusion attention mechanism to obtain the important information from different views. Then, in order to obtain high-quality links and richer nodes information, we introduce a kind of virtual nodes called hypernodes to construct heterogeneous hypergraph of miRNAs and diseases. Finally, we employ the attention mechanism to fuse the outputs of graph convolutional networks, predicting miRNA-disease associations. To verify the effectiveness of this method, we carry out a series of experiments on the Human MicroRNA Disease Database (HMDD v3.2). The experimental results show that AMHMDA has good performance compared with other methods. In addition, the case study results also fully demonstrate the reliable predictive performance of AMHMDA.

Predicting miRNA-Disease Associations via Node-Level Attention Graph Auto-Encoder.

Previous studies have confirmed microRNA (miRNA), small single-stranded non-coding RNA, participates in various biological processes and plays vital roles in many complex human diseases. Therefore, developing an efficient method to infer potential miRNA disease associations could greatly help understand operational mechanisms for diseases at the molecular level. However, during these early stages for miRNA disease prediction, traditional biological experiments are laborious and expensive. Therefore, this study proposes a novel method called AGAEMD (node-level Attention Graph Auto-Encoder to predict potential MiRNA Disease associations). We first create a heterogeneous matrix incorporating miRNA similarity, disease similarity, and known miRNA-disease associations. Then these matrixes are input into a node-level attention encoder-decoder network which utilizes low dimensional dense embeddings to represent nodes and calculate association scores. To verify the effectiveness of the proposed method, we conduct a series of experiments on two benchmark datasets (the Human MicroRNA Disease Database v2.0 and v3.2) and report the averages over 10 runs in comparison with several state-of-the-art methods. Experimental results have demonstrated the excellent performance of AGAEMD in comparison with other methods. Three important diseases (Colon Neoplasms, Lung Neoplasms, Lupus Vulgaris) were applied in case studies. The results comfirm the reliable predictive performance of AGAEMD.

MiRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning.

During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations.

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.