miRNA-disease Associations Research Articles

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

NMCMDA: neural multicategory MiRNA-disease association prediction.

There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA-disease associations are time-saving and cost-effective that are highly desired for us. We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA-disease association prediction (NMCMDA) for predicting multiple-category miRNA-disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA-disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA-disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA-disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

Combined embedding model for MiRNA-disease association prediction

BackgroundCumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results.ResultsWe present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in this article. The combined embedding information of miRNA and disease is composed of pair embedding and node embedding. Compared with the previous heterogeneous network methods that are merely node-centric to simply compute the similarity of miRNA and disease, our method fuses pair embedding to pay more attention to capturing the features behind the relative information, which models the fine-grained pairwise relationship better than the previous case when each node only has a single embedding. First, we construct the heterogeneous network from supported miRNA-disease pairs, disease semantic similarity and miRNA functional similarity. Given by the above heterogeneous network, we find all the associated context paths of each confirmed miRNA and disease. Meta-paths are linked by nodes and then input to the gate recurrent unit (GRU) to directly learn more accurate similarity measures between miRNA and disease. Here, the multi-head attention mechanism is used to weight the hidden state of each meta-path, and the similarity information transmission mechanism in a meta-path of miRNA and disease is obtained through multiple network layers. Second, pair embedding of miRNA and disease is fed to the multi-layer perceptron (MLP), which focuses on more important segments in pairwise relationship. Finally, we combine meta-path based node embedding and pair embedding with the cost function to learn and predict miRNA-disease association. The source code and data sets that verify the results of our research are shown at https://github.com/liubailong/CEMDA.ConclusionsThe performance of CEMDA in the leave-one-out cross validation and fivefold cross validation are 93.16% and 92.03%, respectively. It denotes that compared with other methods, CEMDA accomplishes superior performance. Three cases with lung cancers, breast cancers, prostate cancers and pancreatic cancers show that 48,50,50 and 50 out of the top 50 miRNAs, which are confirmed in HDMM V2.0. Thus, this further identifies the feasibility and effectiveness of our method.

An improved unsupervised learning approach for potential human microRNA–disease association inference using cluster knowledge

Recently, microRNAs (miRNAs) have been shown to play significant roles in the progression of major human diseases. Identifying associations between human diseases and miRNAs using computational tools have attracted considerable attention. Experimental identification and validation of disease–miRNA associations are rare and time consuming. We propose a computational method for predicting associations to understand pathogenicity and uncover prognosis markers. Existing methods employed approaches based on network and machine learning to predict miRNA disease. However, these approaches do not consider the cluster information between miRNAs. Performance results of these methods’ predictions are not satisfactory due to more number of false positives and false negatives. The proposed methodology comprises of two phases: first, we use miRNA cluster information in addition to miRNA-functional similarity and disease semantic similarity. MiRNAs and diseases are placed in the same cluster by transforming the miRNA–disease association data matrix into a low-rank model using Principal Component Analysis (PCA). Second, the Adamic/Adar index was applied that computes the closeness of miRNAs and diseases based on shared neighbors, improving the prediction results. The problem of overestimation is resolved by incorporating similarity information about miRNA and disease. Case studies on Leukemia, Carcinoma, Glioma, Pancreatic Neoplasms, and Melanoma exhibit satisfactory performance with Area Under Curve (AUC) values ranging from 0.736 to 0.834. miRNAs predicted using the proposed method are ascertained to be matching the known associations found in the database of HMDD V3.2, dbDEMC V2.0, and PhenomiR V2.0.

A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder.

MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical role in many biological processes, such as cell growth, development, differentiation and aging. Increasing studies have revealed that miRNAs are closely involved in many human diseases. Therefore, the prediction of miRNA-disease associations is of great significance to the study of the pathogenesis, diagnosis and intervention of human disease. However, biological experimentally methods are usually expensive in time and money, while computational methods can provide an efficient way to infer the underlying disease-related miRNAs. In this study, we propose a novel method to predict potential miRNA-disease associations, called SVAEMDA. Our method mainly consider the miRNA-disease association prediction as semi-supervised learning problem. SVAEMDA integrates disease semantic similarity, miRNA functional similarity and respective Gaussian interaction profile (GIP) similarities. The integrated similarities are used to learn the representations of diseases and miRNAs. SVAEMDA trains a variational autoencoder based predictor by using known miRNA-disease associations, with the form of concatenated dense vectors. Reconstruction probability of the predictor is used to measure the correlation of the miRNA-disease pairs. Experimental results show that SVAEMDA outperforms other stat-of-the-art methods. AUC values of SVAEMDA of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV) are 0.9464 and 0.9428 respectively. In addition, case studies of three common human diseases indicate that SVAEMDA obtains 100 percent of the top 50 predicted candidates in the benchmark databases. Therefore, SVAEMDA can efficiently and accurately predict the potential associations between diseases and miRNAs.

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Background: MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly. Objective: Thus, computational methods are urgently needed to effectively predict miRNA-disease associations. Methods: In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were predicted by the bi-random walk model. Results: To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and 5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively. Conclusion: The fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease associations.

Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma.

Comparisons of MicroRNA Set Enrichment Analysis Tools on Cancer De-regulated miRNAs from TCGA Expression Datasets

Background: De-regulation of microRNAs (miRNAs) is closely related to many complex diseases, including cancers. In The Cancer Genome Atlas (TCGA), hundreds of differentially expressed miRNAs are stored for each type of cancer, which are hard to be intuitively interpreted. To date, several miRNA set enrichment tools have been tailored to predict the potential disease associations and functions of de-regulated miRNAs, including the miRNA Enrichment Analysis and Annotation tool (miEAA) and Tool for Annotations of human MiRNAs (TAM1.0 &TAM 2.0). However, independent benchmarking of these tools is warranted to assess their effectiveness and robustness, and the relationship between enrichment analysis results and the prognosis significance of cancers. Methods: Based on differentially expressed miRNAs from expression profiles in TCGA, we performed a series of tests and a comprehensive comparison of the enrichment analysis results of miEAA, TAM 1.0 and TAM 2.0. The work focused on the performance of the three tools, disease similarity based on miRNA-disease associations from the enrichment analysis results, the relationship between the overrepresented miRNAs from enrichment analysis results and the prognosis significance of cancers. Results: The main results show that TAM 2.0 is more likely to identify the regulatory disease’s functions of de-regulated miRNA; it is feasible to calculate disease similarity based on enrichment analysis results of TAM 2.0; and there is weak positive correlation between the occurrence frequency of miRNAs in the TAM 2.0 enrichment analysis results and the prognosis significance of the cancer miRNAs. Conclusion: Our comparison results not only provide a reference for biomedical researchers to choose appropriate miRNA set enrichment analysis tools to achieve their purpose but also demonstrate that the degree of overrepresentation of miRNAs could be a supplementary indicator of the disease similarity and the prognostic effect of cancer miRNAs.

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.

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association

It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.

KRLSMDA: Identifying Human miRNA–Disease Association Based on Similarity and Kronecker Regularized Least Squares Method

A growing number of studies have suggested that miRNAs (microRNAs) have associations with human diseases, the design and discovery of drug. But so far, we do not yet fully understand the molecular mechanism of miRNAs in the development of human diseases. Predicting miRNA-disease associations is helpful for understanding the molecular mechanism of miRNAs in the development of human diseases. However, wet-lab experiments are time-consuming and need higher costs to discover miRNA-disease associations. Some computational methods are proposed for predicting miRNA-disease associations, but the prediction performance of these methods needs to be further improved. In this study, we propose a new computational model (KRLSMDA) based on similarity and the Kronecker Regularized Least Squares algorithm. In KRLSMDA, the miRNA functional similarity, the miRNA sequence similarity and the Gaussian Interaction Profile (GIP) kernel similarity are integrated into the comprehensive miRNA similarity. Then we compute the disease semantic similarity, disease functional similarity and the GIP kernel similarity to construct the comprehensive disease similarity based on the disease semantic information, the disease functional information and known miRNA-disease associations, respectively. Finally, the kronecker regularized least squares algorithm is used to predict hidden miRNA-disease associations. The experimental results show that KRLSMDA has achieved the average Area Under the Curve (AUC) values of 0.9181±0.032 and 0.9267±0.022 in 5-fold Cross-Validation (5CV) and 10-fold Cross-Validation (10CV), respectively, which demonstrates KRLSMDA is superior to four competing models. We expect KRLSMDA to be a supplement in the field of biomedical research in the future.

MLMD: Metric Learning for Predicting MiRNA-Disease Associations

The crucial roles played by microRNAs (miRNAs) in regulating various biological functions and in disease incidence have been reported continuously over the past decades. Therefore, the identification of novel disease-related miRNAs could help in understanding human disease etiology and pathogenesis further. Due to the involvement of high cost and more time in clinical experiments, development of accurate and feasible computational models is considered highly significant. Here, we aim to present a novel computational model of metric learning for predicting miRNA-disease associations (MLMD). MLMD aims at learning miRNA-disease metric to unravel not only novel disease-related miRNAs but also the miRNA-miRNA and disease-disease similarities. Comprehensive experimental results clearly proved the outstanding performance of MLMD compared to several state-of-the-art methods. MLMD achieved a reliable AUC score of 0.9106 and 0.8786 in the framework of global and local leave-one-out cross validations (LOOCV), respectively. Furthermore, we implemented case studies on two major human cancers (breast cancer and lung cancer) for comparative analysis with already known disease-related miRNAs. Results revealed the top 50 potential candidates were all disease-related miRNAs based on human public databases and literature analysis. We conclude that MLMD could not only serve as practical and feasible framework for inferring potential miRNA-disease associations, but also provide clues for understanding the human complex diseases.

Adopting a new sample strategy to predict miRNA-disease associations

Exploring unknown miRNA-disease associations by computational tools is a new way to study the correlations between genes and diseases. in this paper, we proposed a new model named ABPUSVM, which consisted of a new sample strategy based on Positive-Unlabeled learning and a prediction model that combined AdaBoost and SVM. When ABPUSVM was applied to predict unknown associations of miRNA-disease, the AUC of 0.9383 improved greatly based on 5 folds cross-validation and showed its excellent performance, which indicated ABPUSVM was significantly better than other classic models. Afterward, a case study confirmed that 46 out of the top predicted 50 miRNAs of breast cancer by ABPUSVM were supported by three databases. whose results showed the dataset by ABPUSVM were significantly better than that of the other methods. All results have shown that ABPUSVM is a promising and potential tool for exploring the associations of miRNA-disease.

DEMLP: DeepWalk Embedding in MLP for miRNA‐Disease Association Prediction

miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA‐disease associations, a lot of high‐efficiency computational methods by computer have been developed, based on a network generated by miRNA‐disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA‐disease network by miRNA‐disease adjacency matrix (MDA). Then, low‐dimensional embedding representation vectors of nodes are learned from the miRNA‐disease network by DeepWalk. Finally, we use these low‐dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA‐disease associations. To evaluate the effectiveness of DEMLP in a miRNA‐disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC‐AUC computed result value of DEMLP is 0.943, and the PR‐AUC value of DEMLP is 0.937. Compared with other state‐of‐the‐art methods, our method shows good performance using only the miRNA‐disease interaction network.

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

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MLPMDA: Multi-layer linear projection for predicting miRNA-disease association

The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence.

PmDNE: Prediction of miRNA-Disease Association Based on Network Embedding and Network Similarity Analysis.

Successful prediction of miRNA-disease association is nontrivial for the diagnosis and prognosis of genetic diseases. There are many methods to predict miRNA and disease, but biological data are numerous and complex, and they often exist in the form of network. How to accurately use the features of miRNA and disease-related biological networks to predict unknown association has always been a challenge. Here, we propose PmDNE, a method based on network embedding and network similarity analysis, to predict the miRNA-disease association. In PmDNE, the structure of network bipartite graph is improved, and a random walk generator is designed. For embedded vectors, 128 dimensions are used, and the accuracy of prediction is significantly improved. Compared with other network embedding methods, PmDNE is comparable and competitive with the state of art methods. Our method can solve the problem of feature extraction, reduce the dimension of features, and improve the efficiency of miRNA-disease association prediction. This method can also be extended to other area for biomedical network prediction.

MRWMDA: A novel framework to infer miRNA-disease associations

MicroRNAs (miRNAs) are widely involved in a series of significant biological processes, which have been revealed and verified by accumulating experimental studies. The computational inference of the correlation between miRNAs and diseases is essential to facilitate the detection of disease biomarkers for disease diagnosis, prevention, treatment and prognosis. In this paper, a model with Multiple use of Random Walk with restart algorithm was introduced for the prediction of the MiRNA-Disease Association (MRWMDA). Based on diverse similarity measures, the model first implemented the random walk with restart (RWR) algorithm on the integrated similarity network to construct the topological similarity of miRNAs and diseases, which took full advantage of the network topology information. Then, the RWR algorithm was applied in the miRNA topological similarity network, and a steady probability of each miRNA-disease pair was obtained to prioritize miRNA candidates. In particular, the initial probability of the RWR algorithm was determined by utilizing the combination of the recommendation algorithm and the maximum similarity method. The proposed model achieved significant improvement in prediction compared with previous models, with an AUC of 0.9353 and an AUPR of 0.4809. In addition, case studies of breast neoplasms and lung neoplasms representing different disease types further demonstrated the excellent ability of MRWMDA in detecting potential disease-associated miRNAs. These performance analyses indicated that MRWMDA could be an effective and powerful biological computational tool in relevant biomedical studies.

Prediction of Potential Associations Between miRNAs and Diseases Based on Matrix Decomposition

It is known that miRNA plays an increasingly important role in many physiological processes. Disease-related miRNAs could be potential biomarkers for clinical diagnosis, prognosis, and treatment. Therefore, accurately inferring potential miRNAs related to diseases has become a hot topic in the bioinformatics community recently. In this study, we proposed a mathematical model based on matrix decomposition, named MFMDA, to identify potential miRNA–disease associations by integrating known miRNA and disease-related data, similarities between miRNAs and between diseases. We also compared MFMDA with some of the latest algorithms in several established miRNA disease databases. MFMDA reached an AUC of 0.9061 in the fivefold cross-validation. The experimental results show that MFMDA effectively infers novel miRNA–disease associations. In addition, we conducted case studies by applying MFMDA to three types of high-risk human cancers. While most predicted miRNAs are confirmed by external databases of experimental literature, we also identified a few novel disease-related miRNAs for further experimental validation.

Identification of Small Molecule-miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks.

MicroRNAs (miRNAs) are significant regulators of post-transcriptional levels and have been confirmed to be targeted by small molecule (SM) drugs. It is a novel insight to treat human diseases and accelerate drug discovery by targeting miRNA with small molecules. Computational approaches for discovering novel small molecule-miRNA associations by integrating more heterogeneous network information provide a new idea for the multiple node association prediction between small molecule-miRNA and small molecule-disease associations at a system level. In this study, we proposed a new computational model based on graph regularization techniques in heterogeneous networks, called identification of small molecule-miRNA associations with graph regularization techniques (SMMARTs), to discover potential small molecule-miRNA associations. The novelty of the model lies in the fact that the association score of a small molecule-miRNA pair is calculated by an iterative method in heterogeneous networks that incorporates small molecule-disease associations and miRNA-disease associations. The experimental results indicate that SMMART has better performance than several state-of-the-art methods in inferring small molecule-miRNA associations. Case studies further illustrate the effectiveness of SMMART for small molecule-miRNA association prediction.