Predicting microRNA-disease associations using bipartite local models and hubness-aware regression

Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models

Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K-fold cross validations and case studies seem to be must-have procedures, the value of K, the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance.

HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction

Identifying disease-related miRNAs can improve the understanding of complex diseases. However, experimentally finding the association between miRNAs and diseases is expensive in terms of time and resources. The computational screening of reliable miRNA-disease associations has thus become a necessary tool to guide biological experiments. "Similar miRNAs will be associated with the same disease" is the assumption on which most current miRNA-disease association prediction methods rely; however, biased prior knowledge, and incomplete and inaccurate miRNA similarity data and disease similarity data limit the performance of the model. Here, we propose heuristic learning based on graph neural networks to predict microRNA-disease associations (HLGNN-MDA). We learn the local graph topology features of the predicted miRNA-disease node pairs using graph neural networks. In particular, our improvements to the graph convolution layer of the graph neural network enable it to learn information among homogeneous nodes and among heterogeneous nodes. We illustrate the performance of HLGNN-MDA by performing tenfold cross-validation against excellent baseline models. The results show that we have promising performance in multiple metrics. We also focus on the role of the improvements to the graph convolution layer in the model. The case studies are supported by evidence on breast cancer, hepatocellular carcinoma and renal cell carcinoma. Given the above, the experiments demonstrate that HLGNN-MDA can serve as a reliable method to identify novel miRNA-disease associations.

Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models

Since the problem proposed in late 2000s, microRNA-disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance.

MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA–Disease Association Prediction

MicroRNAs (miRNAs) are small non-coding RNAs that are related to a number of complicated biological processes, and numerous studies have demonstrated that miRNAs are closely associated with many human diseases. In this study, we present a matrix decomposition and similarity-constrained matrix factorization (MDSCMF) to predict potential miRNA–disease associations. First of all, we utilized a matrix decomposition (MD) algorithm to get rid of outliers from the miRNA–disease association matrix. Then, miRNA similarity was determined by utilizing similarity kernel fusion (SKF) to integrate miRNA function similarity and Gaussian interaction profile (GIP) kernel similarity, and disease similarity was determined by utilizing SKF to integrate disease semantic similarity and GIP kernel similarity. Furthermore, we added L₂ regularization terms and similarity constraint terms to non-negative matrix factorization to form a similarity-constrained matrix factorization (SCMF) algorithm, which was applied to make prediction. MDSCMF achieved AUC values of 0.9488, 0.9540, and 0.8672 based on fivefold cross-validation (5-CV), global leave-one-out cross-validation (global LOOCV), and local leave-one-out cross-validation (local LOOCV), respectively. Case studies on three common human diseases were also implemented to demonstrate the prediction ability of MDSCMF. All experimental results confirmed that MDSCMF was effective in predicting underlying associations between miRNAs and diseases.

Research progress of miRNA-disease association prediction and comparison of related algorithms

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA-disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA-disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D5430 and D6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA-disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future.

TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction

The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association. TSMDA significantly outperforms similar methods, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 and 0.982 under 5-fold cross-validation and blind test, respectively. We also demonstrate the capability of the method to uncover potential miRNA-disease associations in breast, prostate, and lung cancers, as case studies. We believe TSMDA will be an invaluable tool for the community to explore and prioritize potentially new miRNA-disease associations for further experimental characterization. The method was made available as a freely accessible and user-friendly web interface at http://biosig.unimelb.edu.au/tsmda/.

Data Integration Using Tensor Decomposition for The Prediction of miRNA-Disease Associations

Dysfunction of miRNAs has an important relationship with diseases by impacting their target genes. Identifying disease-related miRNAs is of great significance to prevent and treat diseases. Integrating information of genes related miRNAs and/or diseases in calculational methods for miRNA-disease association studies is meaningful because of the complexity of biological mechanisms. Therefore, in this study, we propose a novel method based on tensor decomposition, termed TDMDA, to integrate multi-type data for identifying pathogenic miRNAs. First, we construct a three-order association tensor to express the associations of miRNA-disease pairs, the associations of miRNA-gene pairs, and the associations of gene-disease pairs simultaneously. Then, a tensor decomposition-based method with auxiliary information is applied to reconstruct the association tensor for predicting miRNA-disease associations, and the auxiliary information includes biological similarity information and adjacency information. The performance of TDMDA is compared with other advanced methods under 5-fold cross-validations. The experimental results indicate the TDMDA is a competitive method.

ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.

MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information

MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. In this study, we developed MDA-CF to identify underlying miRNA-disease associations based on a cascade forest model. In this method, multi-source information was integrated to represent miRNAs and diseases comprehensively, and the autoencoder was utilized for dimension reduction to obtain the optimal feature space. The cascade forest model was then employed for miRNA-disease association prediction. As a result, the average AUC of MDA-CF was 0.9464 on HMDD v3.2 in five-fold cross-validation. Compared with previous computational methods, MDA-CF performed better on HMDD v2.0 with an average AUC of 0.9258. Moreover, MDA-CF was implemented to investigate colon neoplasm, breast neoplasm, and gastric neoplasm, and 100%, 86%, 88% of the top 50 potential miRNAs were validated by authoritative databases. In conclusion, MDA-CF appears to be a reliable method to uncover disease-associated miRNAs. The source code of MDA-CF is available at https://github.com/a1622108/MDA-CF.

MicroRNA-203-3p inhibits the proliferation, invasion and migration of pancreatic cancer cells by downregulating fibroblast growth factor 2

Aberrant expression of fibroblast growth factor 2 (FGF2) is a major cause of poor prognosis in patients with pancreatic cancer. MicroRNA (miRNA/miR) miR-203-3p is a newly identified miRNA that can affect the biological behavior of tumors. The present study investigated the function of miR-203-3p on the regulation of FGF2 expression, and its role in pancreatic cancer cell proliferation, apoptosis, invasion and migration. Reverse transcription-quantitative PCR was used to determine the mRNA expression levels of miR-203-3p and FGF2 in vitro. Cell Counting Kit-8, Annexin V-APC/7-AAD double-staining Apoptosis Detection kit, wound healing and Transwell assays were used to determine the proliferation, apoptosis, migration and invasion of pancreatic cancer cells. The binding of miR-203-3p to FGF2 was assessed by a luciferase reporter assay. The results demonstrated that miR-203-3p expression was downregulated in pancreatic cancer cells. Gain- and loss-of-function experiments indicated that miR-203-3p inhibited the proliferation, migration and invasion, and promoted the apoptosis of pancreatic cancer cells in vitro. In addition, it was found that alteration of miR-203-3p abolished the promoting effects of FGF2 on pancreatic cancer cells. The present study demonstrated that FGF2 significantly promoted the proliferation, invasion and migration of pancreatic cancer cells. The mechanism involved the binding of miR-203-3p to the 3′-untranslated region of FGF2 mRNA, resulting in the downregulation of FGF2. In conclusion, miR-203-3p inhibited FGF2 expression, regulated the proliferation and inhibited the invasion and migration of pancreatic cancer cells.

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA-disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA-disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease-miRNA association prediction model.

A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network

Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA-disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA-disease.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

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.

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

BACKGROUND

The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.

RESULTS

Here, we present a computational framework based on graph Laplacian regularized L_{2, 1}-nonnegative matrix factorization (GRL_{2, 1}-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL_2,1-NMF framework was used to predict links between microRNAs and diseases.

CONCLUSIONS

The new method (GRL_{2, 1}-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL_{2, 1}-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model’s average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

IMPMD: An Integrated Method for Predicting Potential Associations Between miRNAs and Diseases

Background

With the rapid development of biological research, microRNAs (miRNAs) have increasingly attracted worldwide attention. The increasing biological studies and scientific experiments have proven that miRNAs are related to the occurrence and development of a large number of key biological processes which cause complex human diseases. Thus, identifying the association between miRNAs and disease is helpful to diagnose the diseases. Although some studies have found considerable associations between miRNAs and diseases, there are still a lot of associations that need to be identified. Experimental methods to uncover miRNA-disease associations are time-consuming and expensive. Therefore, effective computational methods are urgently needed to predict new associations.

Methodology

In this work, we propose an integrated method for predicting potential associations between miRNAs and diseases (IMPMD). The enhanced similarity for miRNAs is obtained by combination of functional similarity, gaussian similarity and Jaccard similarity. To diseases, it is obtained by combination of semantic similarity, gaussian similarity and Jaccard similarity. Then, we use these two enhanced similarities to construct the features and calculate cumulative score to choose robust features. Finally, the general linear regression is applied to assign weights for Support Vector Machine, K-Nearest Neighbor and Logistic Regression algorithms.

Results

IMPMD obtains AUC of 0.9386 in 10-fold cross-validation, which is better than most of the previous models. To further evaluate our model, we implement IMPMD on two types of case studies for lung cancer and breast cancer. 49 (Lung Cancer) and 50 (Breast Cancer) out of the top 50 related miRNAs are validated by experimental discoveries.

Conclusion

We built a software named IMPMD which can be freely downloaded from https://github.com/Sunmile/IMPMD.

miR‑146a‑5p suppresses ATP‑binding cassette subfamily G member 1 dysregulation in patients with refractory Mycoplasma pneumoniae via interleukin 1 receptor‑associated kinase 1 downregulation

In the present study, we examined the function of microRNA (miR)-146a-5p in patients with refractory Mycoplasma pneumoniae pneumonia. In brief, the expression of miR-146a-5p was reduced in patients with refractory Mycoplasma pneumoniae pneumonia. Downregulation of miR-146a-5p reduced inflammation in an in vitro model of refractory Mycoplasma pneumoniae pneumonia, whilst overexpression of miR-146a-5p promoted inflammation. Downregulation of miR-146a-5p induced the protein expression of ATP-binding cassette subfamily G member 1 (ABCG1) and interleukin 1 receptor-associated kinase 1 (IRAK-1), while suppressed expression was observed of the aforementioned proteins following overexpression of miR-146a-5p in an in vitro model of refractory Mycoplasma pneumoniae pneumonia. The administration of small interfering RNA against RXR or IRAK-1 attenuated the effects of miR-146a-5p on inflammation in an in vitro model of refractory Mycoplasma pneumoniae pneumonia. Collectively, these results suggested that miR-146a-5p reduced ABCG1 expression in refractory Mycoplasma pneumoniae pneumonia via downregulation of IRAK-1.

Benchmark of computational methods for predicting microRNA-disease associations

BACKGROUND

A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness.

RESULTS

Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations.

CONCLUSION

Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction

Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.

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.

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.

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases

Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA–disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA–disease associations, as well as the integrated similarity between diseases and the integrated similarity between miRNAs. Then, a new computational method implementing the newly weighted interactive network was developed for discovering potential miRNA–disease associations (WINMDA) by integrating the T most similar neighbors and the shortest path algorithm. Simulation results show that WINMDA can achieve reliable area under the receiver operating characteristics (ROC) curve (AUC) results of 0.9183 ± 0.0007 in 5-fold cross-validation, 0.9200 ± 0.0004 in 10-fold cross-validation, 0.9243 in global leave-one-out cross-validation (LOOCV), and 0.8856 in local LOOCV. Furthermore, case studies of colon neoplasms, gastric neoplasms, and prostate neoplasms based on the Human microRNA Disease Database (HMDD) database were implemented, for which 94% (colon neoplasms), 96% (gastric neoplasms), and 96% (prostate neoplasms) of the top 50 predicting miRNAs were confirmed by recent experimental reports, which also demonstrates that WINMDA can effectively uncover potential miRNA–disease associations.