GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

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.

iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints

Growing evidence proves that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, the malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time- and money-consuming biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-disease associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraints. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraints. To the best of our knowledge, the iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that the iSnoDi-LSGT predictor can effectively predict unknown snoRNA-disease associations. The web server of the iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT.

MHDMF: Prediction of miRNA-disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network

A growing number of works have proved that microRNAs (miRNAs) are a crucial biomarker in diverse bioprocesses affecting various diseases. As a good complement to high-cost wet experiment-based methods, numerous computational prediction methods have sprung up. However, there are still challenges that exist in making effective use of high false-negative associations and multi-source information for finding the potential associations. In this work, we develop an end-to-end computational framework, called MHDMF, which integrates the multi-source information on a heterogeneous network to discover latent disease-miRNA associations. Since high false-negative exist in the miRNA-disease associations, MHDMF utilizes the multi-source Graph Convolutional Network (GCN) to correct the false-negative association by reformulating the miRNA-disease association score matrix. The score matrix reformulation is based on different similarity profiles and known associations between miRNAs, genes, and diseases. Then, MHDMF employs Deep Matrix Factorization (DMF) to predict the miRNA-disease associations based on reformulated miRNA-disease association score matrix. The experimental results show that the proposed framework outperforms highly related comparison methods by a large margin on tasks of miRNA-disease association prediction. Furthermore, case studies suggest that MHDMF could be a convenient and efficient tool and may supply a new way to think about miRNA-disease association prediction.

DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples

Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases. The existing databases about the miRNA-disease associations (MDAs) only provide the known MDAs, which can be regarded as positive samples. However, the unknown MDAs are not sufficient to regard as reliable negative samples. To deal with this uncertainty, we proposed a convolutional neural network (CNN) framework, named DNRLCNN, based on a latent feature matrix extracted by only positive samples to predict MDAs. First, by only considering the positive samples into the calculation process, we captured the latent feature matrix for complex interactions between miRNAs and diseases in low-dimensional space. Then, we constructed a feature vector for each miRNA and disease pair based on the feature representation. Finally, we adopted a modified CNN for the feature vector to predict MDAs. As a result, our model achieves better performance than other state-of-the-art methods which based CNN in fivefold cross-validation on both miRNA-disease association prediction task (average AUC of 0.9030) and miRNA-phenotype association prediction task (average AUC of 0. 9442). In addition, we carried out case studies on two human diseases, and all the top-50 predicted miRNAs for lung neoplasms are confirmed by HMDD v3.2 and dbDEMC 2.0 databases, 98% of the top-50 predicted miRNAs for heart failure are confirmed. The experiment results show that our model has the capability of inferring potential disease-related miRNAs.

Predicting multiple types of MicroRNA-disease associations based on tensor factorization and label propagation

MicroRNAs (miRNAs) play important regulatory roles in the pathogenesis and progression of diseases. Most existing bioinformatics methods only study miRNA-disease binary association prediction. However, there are many types of associations between miRNA and disease. In addition, the miRNA-disease-type association dataset has inherent noise and incompleteness. In this paper, a novel method based on tensor factorization and label propagation (TFLP) is proposed to alleviate the above problems. First, as an effective tensor factorization method, tensor robust principal component analysis (TRPCA) is applied to the original multiple-type miRNA-disease associations to obtain a clean and complete low-rank prediction tensor. Second, the Gaussian interaction profile (GIP) kernel is used to describe the similarity of disease pairs and the similarity of miRNA pairs. Then, they are combined with disease semantic similarity and miRNA functional similarity to obtain an integrated disease similarity network and an integrated miRNA similarity network, respectively. Finally, the low-rank association tensor and the biological similarity as auxiliary information are introduced into label propagation. The prediction performance of the algorithm is improved by iterative propagation of labeled information to unlabeled samples. Extensive experiments reveal that the proposed TFLP method outperforms other state-of-the-art methods for predicting multiple types of miRNA-disease associations. The data and source codes are available at https://github.com/nayu0419/TFLP.

WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data

The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate diagnoses and treatments. However, it is still a challenge to effectively integrate multiple omics data to establish cancer subtypes. In this paper, we propose an unsupervised integration method, named weighted multi-view low rank representation (WMLRR), to identify cancer subtypes from multiple types of omics data. Given a group of patients described by multiple omics data matrices, we first learn a unified affinity matrix which encodes the similarities among patients by exploring the sparsity-consistent low-rank representations from the joint decompositions of multiple omics data matrices. Unlike existing subtype identification methods that treat each omics data matrix equally, we assign a weight to each omics data matrix and learn these weights automatically through the optimization process. Finally, we apply spectral clustering on the learned affinity matrix to identify cancer subtypes. Experiment results show that the survival times between our identified cancer subtypes are significantly different, and our predicted survivals are more accurate than other state-of-the-art methods. In addition, some clinical analyses of the diseases also demonstrate the effectiveness of our method in identifying molecular subtypes with biological significance and clinical relevance.

RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method

With the rapid development of biotechnology and computer technology, increasing studies have shown that the occurrence of many diseases in the human body is closely related to the dysfunction of miRNA, and the relationship between them has become a new research hotspot. Exploring disease-related miRNAs information provides a new perspective for understanding the etiology and pathogenesis of diseases. In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA-disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are superior to the other methods. Besides, the prediction performance of RSCMDA is further confirmed by the case study on lung Neoplasms, because 94% of the top 50 miRNAs predicted by the RSCMDA method are confirmed from the existing biological databases or research results. All the results show that RSCMDA is a reliable and effective framework, which can be used as new technology to explore the relationship between miRNA and disease.

iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion

Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately. However, it is tremendously laborious and time-consuming to discover disease-related circRNAs with conventional biological experiments. In this study, we develop an integrative framework, called iCDA-CMG, to predict potential associations between circRNAs and diseases. By incorporating multi-source prior knowledge, including known circRNA-disease associations, disease similarities and circRNA similarities, we adopt a collective matrix completion-based graph learning model to prioritize the most promising disease-related circRNAs for guiding laborious clinical trials. The results show that iCDA-CMG outperforms other state-of-the-art models in terms of cross-validation and independent prediction. Moreover, the case studies for several representative cancers suggest the effectiveness of iCDA-CMG in screening circRNA candidates for human diseases, which will contribute to elucidating the pathogenesis mechanisms and unveiling new opportunities for disease diagnosis and targeted therapy.

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex. The computational network should consider the local and global influence of neighborhoods from the network. Furthermore, predicting disease-related miRNAs without any known associations is also very important. This study presents a new computational method that constructs a heterogeneous network composed of a miRNA similarity network, disease similarity network, and known miRNA-disease association network. The miRNA similarity considers the miRNAs and their possible families and clusters. The information of each node in heterogeneous network is obtained by aggregating neighborhood information with graph convolutional networks (GCNs), which can pass the information of a node to its intermediate and distant neighbors. Disease-related miRNAs with no known associations can be predicted with the reconstructed heterogeneous matrix. We apply 5-fold cross-validation, leave-one-disease-out cross-validation, and global and local leave-one-out cross-validation to evaluate our method. The corresponding areas under the curves (AUCs) are 0.9616, 0.9946, 0.9656, and 0.9532, confirming that our approach significantly outperforms the state-of-the-art methods. Case studies show that this approach can effectively predict new diseases without any known miRNAs.

Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs

Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

A Novel Computational Model for Predicting microRNA-Disease Associations Based on Heterogeneous Graph Convolutional Networks

Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA-disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA-disease associations (HGCNMDA), which is based on known human protein-protein interaction (PPI) and integrates four biological networks: miRNA-disease, miRNA-gene, disease-gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA-disease interactions.

Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.

Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks

Identifying disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Much effort has been devoted to discovering the underlying associations between miRNAs and diseases. However, most studies mainly focus on designing advanced methods to improve prediction accuracy while neglecting to investigate the link predictability of the relationships between miRNAs and diseases. In this work, we construct a heterogeneous network by integrating neighborhood information in the neural network to predict potential associations between miRNAs and diseases, which also consider the imbalance of datasets. We also employ a new computational method called a neural network model for miRNA-disease association prediction (NNMDA). This model predicts miRNA-disease associations by integrating multiple biological data resources. Comparison of our work with other algorithms reveals the reliable performance of NNMDA. Its average AUC score was 0.937 over 15 diseases in a 5-fold cross-validation and AUC of 0.8439 based on leave-one-out cross-validation. The results indicate that NNMDA could be used in evaluating the accuracy of miRNA-disease associations. Moreover, NNMDA was applied to two common human diseases in two types of case studies. In the first type, 26 out of the top 30 predicted miRNAs of lung neoplasms were confirmed by the experiments. In the second type of case study for new diseases without any known miRNAs related to it, we selected breast neoplasms as the test example by hiding the association information between the miRNAs and this disease. The results verified 50 out of the top 50 predicted breast-neoplasm-related miRNAs.

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.

LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

Lots of research findings have indicated that miRNAs (microRNAs) are involved in many important biological processes; their mutations and disorders are closely related to diseases, therefore, determining the associations between human diseases and miRNAs is key to understand pathogenic mechanisms. Existing biological experimental methods for identifying miRNA–disease associations are usually expensive and time consuming. Therefore, the development of efficient and reliable computational methods for identifying disease-related miRNAs has become an important topic in the field of biological research in recent years. In this study, we developed a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method (LSGSP). This integrates experimentally validated miRNA–disease associations, disease semantic similarity scores, miRNA functional scores, and miRNA family information to build a new disease similarity network and miRNA similarity network, and then obtains the global similarities of these networks through calculating the Laplacian score of the graphs, based on which the miRNA–disease weighted network can be constructed through combination with the miRNA–disease Boolean network. Finally, the miRNA–disease score was obtained via projecting the miRNA space and disease space onto the miRNA–disease weighted network. Compared with several other state-of-the-art methods, using leave-one-out cross validation (LOOCV) to evaluate the accuracy of LSGSP with respect to a benchmark dataset, prediction dataset and compare dataset, LSGSP showed excellent predictive performance with high AUC values of 0.9221, 0.9745 and 0.9194, respectively. In addition, for prostate neoplasms and lung neoplasms, the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the updated HMDD, miR2Disease, and dbDEMC databases) reached 96% and 100%, respectively. Similarly, for isolated diseases (diseases not associated with any miRNAs), the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the above-mentioned three databases) reached 98% and 100%, respectively. These results further indicate that LSGSP can effectively predict potential associations between miRNAs and diseases.

Lots of research findings have indicated that the mutations and disorders of miRNAs (microRNAs) are closely related to diseases. Therefore, determining the associations between human diseases and miRNAs is key to understand the pathogenic mechanisms.

MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.