 Research article
 Open Access
 Published:
A network embeddingbased multiple information integration method for the MiRNAdisease association prediction
BMC Bioinformatics volume 20, Article number: 468 (2019)
Abstract
Background
MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNAdisease associations makes contributions to understanding the molecular mechanism of human diseases. Existing stateoftheart methods make use of miRNAtarget associations, miRNAfamily associations, miRNA functional similarity, disease semantic similarity and known miRNAdisease associations, but the known miRNAdisease associations are not well exploited.
Results
In this paper, a network embeddingbased multiple information integration method (NEMII) is proposed for the miRNAdisease association prediction. First, known miRNAdisease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNAfamily associations and disease semantic similarities) to represent miRNAdisease pairs. Third, the prediction models are constructed based on the miRNAdisease pairs by using the random forest. In computational experiments, NEMII achieves highaccuracy performances and outperforms other stateoftheart methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNAdisease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, HighOrder Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE).
Conclusion
We propose a new method, named NEMII, for predicting miRNAdisease associations, which has great potential to benefit the field of miRNAdisease association prediction.
Background
MiRNAs are a kind of small noncoding RNA molecules containing about 22 nucleotides, which are involved in the regulation of posttranscriptional gene expression in plants and animals [1]. MiRNAs are usually considered as negative gene regulators, which regulate the expression of messenger RNAs in a sequencespecific manner and repress the protein translation of their target genes. However, studies showed that miRNAs also act as positive regulators. For example, two wellstudied miRNAs: Let7 and the synthetic miRNA miRcxcr4 induce translation upregulation of target messenger RNAs on cell cycle arrest [2]. The increasing evidence demonstrated that miRNAs play critical roles in important biological processes, such as cell growth [3], tissue differentiation [4], cell proliferation [5], embryonic development and apoptosis [6, 7]. More importantly, plenty of miRNAs have been discovered to be related to a wide range of human diseases, such as breast cancer, heart diseases and cardiovascular disease [8,9,10]. Therefore, the identification of miRNAdisease associations is significant for understanding the molecular mechanisms of human diseases and promoting the diagnosis and treatment of human diseases. Experimental determination of miRNAdisease associations is tremendously expensive and laborious, and has a high failure rate. Therefore, identifying miRNAdisease associations through computational approaches attracts wide attention from scientific communities.
In the past few years, plenty of computational methods have been developed to predict miRNAdisease associations. For example, Sun et al. [11] proposed a method named NTSMDA, which used the network topological information and the networkbased resource allocation algorithm to predict miRNAdisease associations. You et al. [12] constructed a heterogeneous graph by integrating miRNAdisease associations, miRNAmiRNA similarities and diseasedisease similarities, and developed a network pathbased computational method. Chen et al. [13] proposed a method called RKNNMDA, which implemented knearestneighbor algorithm to select candidate miRNAs (or diseases) and used ranking support vector machine (SVM) to rank candidates and make predictions. Xiao et al. [14] proposed a graph regularized nonnegative matrix factorization method called GRNMF, which integrated the disease semantic information, miRNA functional information and miRNAdisease associations. Chen et al. [15] proposed an inductive matrix completion method named IMCMDA by integrating miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. Luo et al. [16] proposed a novel semisupervised prediction method named MDAGRF based on the graph regularization framework. Chen et al. [17] proposed a bipartite network projectionbased method named BNPMDA based on the ratingintegrated bipartite network recommendation and the known miRNAdisease associations.
Existing stateoftheart methods make use of miRNAtarget associations, miRNAfamily associations, miRNA functional similarity, disease semantic similarity and known miRNAdisease associations. However, the known miRNAdisease associations are not well exploited. To the best of our knowledge, known miRNAdisease associations can be formed as a bipartite network, but features from the network are seldom considered. The network embedding is to learn embedding representations of nodes by preserving the property of the network. Recently, the network embedding methods, such as DeepWalk [18] and node2vec [19], have been applied to many bioinformatics problems and produced good performances. For example, Zong et al. [20] utilized node embeddings learned by DeepWalk in a heterogeneous network to calculate drugdrug similarity and targettarget similarity, and then predicted novel drugtarget associations. Li et al. [21] proposed a similaritybased miRNAdisease prediction method, which used DeepWalk to obtain node embeddings and then calculated cosine similarities. Liu et al. [22] used node2vec to obtain node embeddings, and then utilized them to train random forest model for protein complexes identification.
In this paper, a network embeddingbased multiple information integration method (NEMII) is proposed for the miRNAdisease association prediction. First, known miRNAdisease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn node embeddings in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases to represent miRNAdisease pairs. Third, prediction models are constructed based on the miRNAdisease pairs by using random forest. In computational experiments, NEMII achieves highaccuracy performances and outperforms other stateoftheart methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding methods for the miRNAdisease association prediction, and the embedding method SDNE outperforms other popular network embedding methods: DeepWalk, HighOrder Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE).
Results
Evaluation metrics
In a miRNAdisease bipartite network, nonassociation miRNAdisease pairs are much more than association pairs. The miRNAdisease association prediction is a semisupervised learning task, and the key point is to predict undiscovered miRNAdisease associations from all nonassociation miRNAdisease pairs. We adopt fivefold crossvalidation to evaluate the performances of prediction models. Additional file 1: Figure S1 shows how to implement fivefold crossvalidation. The known miRNAdisease associations are randomly equally divided into five subsets. In each fold, one subset of associations is removed, and we can train a prediction model only based on the remaining four subsets of associations. In the stage of training, SDNE is to learn embeddings of miRNAs and diseases from the network with remaining four subsets of associations. Then, the embeddings are combined with biological features about miRNAs and diseases to represent miRNAdisease pairs. Four subsets of associations (miRNAdisease pairs) are naturally used as positive instances. For the semisupervised learning task, all other pairs (nonassociation) can be used as negative instances. Therefore, a RFbased prediction model is constructed. In the stage of prediction, the prediction model makes prediction for all nonassociation miRNAdisease pairs, which include the removed associations and real nonassociation pairs. Then, the prediction scores and their real labels about these pairs are used to calculate the metric scores. To avoid the bias of data split, we implement 10 runs of fivefold crossvalidation for each model, and average performance is adopted.
We adopt several evaluation metrics: the area under the preciserecall curve (AUPR) and the area under the receiveroperating characteristic curve (AUC), F1measure (F1), recall (REC) and precision (PRE), accuracy (ACC) and specificity (SPEC).
Discussion of NEMII
NEMII combines three types of feature vectors for describing miRNAdisease pairs, i.e. vectors based on miRNAfamily associations, vectors based on disease semantic similarity and vectors based on Structural Deep Network Embedding (SDNE). Vectors based on Structural Deep Network Embedding (SDNE) are obtained from the known miRNAdisease bipartite network.
First, we try to discuss the influence of different feature combinations on the performance of NEMII. We consider different combinations of these three features: miRNAfamily, disease similarity and SDNE feature by combining their feature vectors, and build the corresponding random forestbased prediction models. All models based on feature combinations are evaluated by 10 runs of fivefold crossvalidation, and the results are shown in Table 1. In general, combinations with SDNE feature produce better performance than combinations without SDNE feature. For example, the AUPR score of combination 2 (without SDNE feature) is around 57% lower than that of the other four combinations (with SDNE feature). The AUC score of combination 2 (without SDNE feature) is 14% lower than that of other four combinations (with SDNE feature). Therefore, the results suggest that SDNE feature plays an important role in the prediction of miRNAdisease associations. Moreover, NEMII models which make use of SDNE feature, miRNAfamily feature and disease similarity feature (combination 5) performs better than the models based on other combinations, indicating that the proposed method can well combine diverse features to achieve highaccuracy performances. To further demonstrate the advantage of NEMII, we conduct the statistical analysis to test the difference between NEMII and other feature combinations in terms of AUPR scores. The results show that although there is no significant difference between NEMII and the model based on combination 3 (pvalue = 0.3449 by oneway ANOVA followed by post hoc Tukey test), and the model based on combination 4 (pvalue = 0.2759), the NEMII model produces significantly better results than the model based on combination 1 (pvalue = 0.001) and the model based on combination 2 (pvalue = 0.001). The results demonstrate that NEMII which integrates SDNE feature, miRNAfamily feature and disease similarity feature can produce good performance in the prediction of miRNAdisease associations.
The known miRNAdisease associations are important factors for predicting unobserved miRNAdisease associations. In order to test the influence of the number of known associations, i.e. data richness, we randomly remove 10, 20, 30% known miRNAdisease associations from our dataset respectively, and then we perform 10 runs of fivefold crossvalidation to evaluate NEMII on the datasets with fewer associations. As shown in Table 2, data richness greatly influenced the performance of our model, and AUPR and AUC scores decrease as more associations are removed. For example, the AUPR score is 0.6104 when there are no associations removed, but it decreases to 0.6001 when removing 10% associations. Then, the AUPR score decreases from 0.5956 (20% associations removed) to 0.5863 (30% associations removed). The AUC score also decreases as associations are removed. More specifically, 10% decrease of associations can lead to around 0.1% decrease of the AUC score. Although the performances of NEMII decrease when reducing associations, NEMII still produces satisfying and robust results in the miRNAdisease predictions. The results demonstrate that SDNE is a robust embedding learning method, and can perform well even if the network becomes sparser.
Comparison with other network embeddings and other classifiers
As discussed in Discussion of NEMII Section, features extracted by the embedding method SDNE are critical for building NEMII models. To demonstrate the advantage of SDNE, we also consider other popular network embedding methods: Laplacian Eigenmaps (LE) [23], HighOrder Proximity preserved Embedding (HOPE) [24] and DeepWalk [18], and compare them with SDNE. LE keeps embeddings of two nodes close when these two nodes have high similarity. HOPE preserves high order proximity by decomposing the similarity matrix and using a generalized Singular Value Decomposition (SVD). DeepWalk uses random walks on graphs to learn latent representations of nodes and encodes them in a continuous space. These embedding methods usually have different parameters, and we set their parameters according to their publication and mainly discuss a common parameter: the dimension of node embeddings.
Here, we discuss the model performance under the different dimensions of node embeddings, ranging from 32 to 512 (2^{k}, k = 5, 6, 7, 8, 9). We respectively adopt these embedding methods to extract embedding features from the network, and then combine them with miRNAfamily feature and disease similarity feature to build similar SDNE models. The results of all models are shown in Fig. 1. The yaxis denotes the AUPR and AUC scores obtained by the corresponding model, and the xaxis denotes different dimensions of node embeddings. Clearly, the model using SDNE embedding leads to better AUPR scores and AUC scores than the models using other three embeddings over the different dimensions. To further demonstrate the advantage of SDNE, we conduct oneway ANOVA followed by post hoc Tukey test to test the difference between SDNE and other embedding methods in terms of AUPR scores. The results show that the SDNE model produces significantly better results than the HOPE model (pvalue = 0.0027) and the LAP model (pvalue = 0.0019). The pvalue between the SDNE model and the DeepWalk model is 0.0507, which suggests that there is no significant difference between the SDNE model and the DeepWalk model. Therefore, we conclude that SDNE method can learn more effective node embeddings in the miRNAdisease bipartite network and performs better than other three methods, because the miRNAdisease bipartite network is sparse and SDNE method was proved to be robust to sparse networks [25]. Anothers reason why SDNE works better than other embedding learning methods is that SDNE combines the autoencoder objective with the Laplacian eigenmaps objective.
Moreover, we observe from Fig. 1 (right) that the SDNE model using 128 dimensions produces the lowest AUC score of 0.9293. In order to avoid overestimating our model, we set the embedding vectors of SDNE as 128 dimensions in this study.
In order to show the advantage of random forest (RF) classifier, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM) are used for comparison. By considering the number of trees ranging from 10 to 500 in a step of 10, we set the number of trees in a RF classifier as 350 according to the performances of corresponding models. We adopt the radial basis function for SVM, and use the grid search to obtain optimal parameters C = 1.0 and gamma = 0.1. The prediction models based on different classifiers are evaluated by fivefold crossvalidation, and the results are shown in Table 3. Clearly, the model using RF classifier (AUC:0.9293, AUPR: 0.6104) performs better than the models using NB (AUC: 0.9103, AUPR: 0.1846), LR (AUC: 0.9023, AUPR: 0.2129) and SVM (AUC: 0.9021, AUPR: 0.0968). The results demonstrate that RF classifier is suitable for the miRNAdisease association prediction, because RF classifier is effective for the imbalanced and highdimensional datasets.
We also implement weighted random forest to build the prediction model and compare it with the conventional random forestbased model. The conventional random forest assigns equal weights to output labels (0,1) predicted by the decision trees of the random forest, while the weighted random forest assigns different weights to output labels (0,1), denoted as w_{i} = n _ samples/(2 ∗ n_{i}), i = {0, 1}, where n _ samples denotes the number of samples in the training set, and n_{i} denotes the number of samples of each label (0,1). The performance of the weighted random forest is shown in Table 3. Compared with the conventional RF, the weighted RF produces better AUC but lower AUPR and F1. In general, the weighted RF and conventional RF have the similar performances in the miRNAdisease association prediction, and thus conventional RF classifier is finally adopted in this work.
Comparison with existing stateoftheart methods
To further demonstrate the advantages of NEMII, we compare it with three stateoftheart methods: PBMDA [12], NTSMDA [11] and GRNMF [14], because they are latest methods with highaccuracy performances. PBMDA constructed a heterogeneous network based on miRNAmiRNA similarity, diseasedisease similarity and known miRNAdisease associations, and then scored miRNAdisease associations by using the number of paths from miRNAs to diseases. NTSMDA considered topological information of the miRNAdisease association network, and used the networkbased resource allocation algorithm. GRNMF integrated disease semantic similarity and miRNA functional similarity, and used a graph regularized nonnegative matrix factorization framework to predict associations. Here, we implement these prediction methods according to their publications, and evaluate all models by using fivefold crossvalidation under same conditions.
As shown in Table 4, NEMII produces the best performances, achieving the AUPR score of 0.6104, and the AUC score of 0.9293. PBMDA, NTSMDA and GRNMF produce the AUPR score of 0.2095, 0.0916 and 0.2446, and the AUC score of 0.9164, 0.8857 and 0.9128 respectively. The AUPR score of NEMII is significantly higher than the other three methods, and the AUC score is also higher than the other three methods. Moreover, we analyze the statistical differences between NEMII and the other three methods in terms of AUC scores, and we observe that there exists a very significant difference between NEMII and other three methods: PBMDA (pvalue = 0.001 by oneway ANOVA followed by post hoc Tukey test), NTSMDA (pvalue = 0.001) and GRNMF (pvalue = 0.001). Therefore, NEMII produces significantly better results than PBMDA, NTSMDA and GRNMF in the crossvalidation experiment.
Further, we compare the predictive performances of four methods for specified diseases. We select three diseases of wide interests: “breast neoplasms”, “lung neoplasms” and “prostatic neoplasms”, and then we explore the results of different methods on these three diseases. Breast neoplasm develops from breast tissues which are highly prevalent in women. Lung neoplasm is a kind of malignant lung neoplasm caused by uncontrolled growth of lung tissue cells. Prostatic neoplasm is a kind of malignant neoplasm occurring in the prostate. We implement 10 runs of fivefold crossvalidation and then obtain the prediction results of all these methods for each disease. As shown in Fig. 2 (left), NEMII produces significantly higher AUPR scores than PBMDA, NTSMDA and GRNMF for all three diseases. For example, the AUPR score for breast neoplasm produced by NEMII is 0.8476, which is 37.78% higher than the AUPR score of 0.5274 produced by PBMDA (the highest among PBMDA, NTSMDA, and GRNMF). And as shown in Fig. 2 (right), NEMII also produces higher AUC scores than PBMDA, NTSMDA and GRNMF for all three diseases. For example, the AUC score of prostatic neoplasm obtained by NEMII is 0.9296, which is 9.76% higher than 0.8389 obtained by GRNMF (the highest among PBMDA, NTSMDA, and GRNMF). Therefore, we can conclude that NEMII outperforms PBMDA, NTSMDA and GRNMF in predicting miRNAs for three specified diseases.
We further investigate what the percentage of removed known miRNAdisease associations could be covered by NEMII and other approaches. We randomly remove 10, 20 and 30% known miRNAdisease associations respectively, then train models based on the remained known and unknown associations and test what percentage of removed associations could be recovered. We use REC values to represent the results. The performances of NEMII and other approaches are shown in Fig. 3. Clearly, NEMII leads to better REC values than other approaches when removing 10, 20 and 30% known associations. To further demonstrate the advantage of NEMII, we conduct oneway ANOVA followed by post hoc Tukey test to test the difference between NEMII and other approaches in terms of REC values. The results show that NEMII produces significantly better results than PBMDA (pvalue = 0.001), NTSMDA (pvalue = 0.001) and GRNMF (pvalue = 0.001). Therefore, we can conclude that NEMII outperforms PBMDA, NTSMDA and GRNMF in recovering the removed known miRNAdisease associations.
Case studies
Here, we use case studies to test the capability of our method for predicting unknown miRNAdisease associations. We build the NEMII model by using all miRNAdisease associations in our dataset, and make predictions for nonassociation miRNAdisease pairs. Since all known associations in HMDD database are used to build models, the predicted associations have to be verified by public literature and other available sources. We list top 10 miRNAdisease associations predicted by NEMII in Table 5, and found evidence to confirm 5 out of them. For example, hsalet7c expression was found to be related to nonatrophic gastritis and atrophic gastritis [26]. Hsamir103a2 expression was downregulated in patients with myelodysplastic syndromes [27]. The expression of hsalet7e has an influence of timedependent suppression on Biliary Atresia [28]. Increased expression of hsamir1179 can inhibit breast cancer cell metastasis by modulating Notch signaling pathway [29]. The expression of hsamir1179 was found to be associated with Hepatocellular Carcinoma. Therefore, our method can help to identify unknown miRNAdisease associations.
Moreover, we predict miRNAs which are associated with three diseases “breast neoplasms”, “lung neoplasms” and “prostatic neoplasms” mentioned in Comparison with existing stateoftheart methods Section. Then we select the top 10 miRNAs associated with each disease, and try to obtain evidence to confirm our findings. As shown in Table 6, we have evidence to support 5 miRNAs associated with breast neoplasms. For example, miR1179 expression was found to be frequently downregulated in breast cancer tissues and cell lines [29]. We find evidences to confirm that 5 miRNAs are associated with lung neoplasms. For example, hsamir376c can suppress nonsmallcell lung cancer cell growth and invasion by targeting LRH1mediated Wnt signaling pathway [35]. Moreover, 4 miRNAs are found to be associated with prostatic neoplasms. For example, hsamir1179 was found to be one of the most highly upregulated miRNAs from the observation of micro dissected prostate tumor cells [39], and hsamir10a was found to be one of the most highly expressed miRNAs in prostate tumors [39]. These evidence shows that these diseaserelated miRNAs have close relationships with breast neoplasms, lung neoplasms and prostatic neoplasms and may be of potential use in the diagnosis of these diseases. Therefore, NEMII is useful for predicting miRNAs associated with given diseases.
Conclusion
The identification of miRNAdisease associations plays an important role in furthering understanding the molecular mechanism of many human diseases. In this work, we propose a novel computational method, called NEMII, to predict unknown miRNAdisease associations. Different from existing methods which mainly make use of biological features of miRNAs and diseases, NEMII extracts the embedding representations of miRNAs and diseases from the miRNAdisease bipartite network, and further combines them with biological features to build the prediction model. Experimental results reveal that NEMII performs better than the models using biological features alone and models using embedding representations alone, and SDNE produces better results than using other network embedding methods. NEMII also produces better results when compared with other stateoftheart methods. Case studies show that NEMII can predict novel miRNAdisease associations, and can predict miRNAs associated with given diseases. In conclusion, NEMII is a promising method for the miRNAdisease association prediction.
Methods
Datasets
There are several databases about miRNAdisease associations, e.g. the human microRNA disease database (HMDD) [42], the database of differentially expressed miRNAs in human cancers (dbDEMC) [43] and the database for microRNA deregulation in human disease (miR2Disease) [44]. The databases lay the basis for developing computational methods to predict unobserved miRNAdisease associations.
In this study, we compile our datasets from HMDD database v2.0, miRBase and Medical Subject Heading (MeSH). HMDD [45] is a database which contains human miRNAdisease associations and comprehensive annotations. We downloaded experimentally confirmed miRNAdisease associations from HMDD, including 578 miRNAs, 383 diseases and 6448 associations. The database miRBase [46] is an online repository of miRNA sequences and the experimental miRNAfamily relationships. We collected miRNAfamily associations from miRBase, including 17,613 miRNAs and 1983 families; a miRNA belongs to a family and a family contains more than one miRNA. MeSH is a comprehensive medical vocabulary, which is useful for exploring the relationship between different diseases. We downloaded disease descriptors from MeSH. The relationships of diseases can be transformed into a directed acyclic graph (DAG), and the nodes of a DAG represent the diseases and the edges represent the relationships of different diseases. DAGs can be used to calculate disease semantic similarity [12].
We removed miRNAs without family information as well as diseases without MeSH descriptors. Finally, we obtained 4479 miRNAdisease associations between 412 miRNAs and 314 diseases, 278 miRNAfamily associations between 412 miRNAs and 278 families, and MeSH descriptors for 314 diseases.
Pipeline of network embeddingbased multiple information integration method
For the following study, we first introduce several mathematical notations. Given miRNAs M = {M_{1}, M_{2}, …, M_{m}}, diseases D = {D_{1}, D_{2}, ⋯, D_{n}} and miRNAdisease associations, our task is to predict unknown miRNAdisease associations based on known associations and biological features. The associations between m miRNAs and n diseases can be represented by a binary matrix A, in which each row represents a miRNA and each column represents a disease. If the ith miRNA is associated with the jth disease, A_{ij} = 1; otherwise, A_{ij} = 0. Formally, m miRNAs, n diseases and their known associations can be formulated as a network, in which miRNAs and diseases are taken as nodes and their associations are taken as edges. The network can be represented by a (m + n) × (m + n) adjacency matrix G, defined as \( G=\left[\begin{array}{cc}0& A\\ {}{A}^T& 0\end{array}\right] \).
The studies [47,48,49,50,51,52,53] have revealed that combining diverse information helps to improve the accuracy of prediction models in bioinformatics. The network embeddingbased multiple information integration method (NEMII) is to combine biological features of miRNAs and diseases with their embedding representations. As described in Fig. 4, NEMII takes several steps to construct a prediction model. First, miRNAfamily associations are used to represent miRNAs; MeSH information of diseases are used to calculate diseasedisease similarity and then represent diseases. Second, the known miRNAdisease associations are formulated as a bipartite network, and node embeddings in the bipartite network are learned by using SDNE and then used to represent miRNAs and diseases. Third, all representations of miRNAs and diseases are combined to represent miRNAdisease pairs. Finally, a prediction model is constructed based on the miRNAdisease pairs by using random forest.
Constructing feature vectors to represent miRNAdisease pairs
In order to predict miRNAdisease associations, we should use a reasonable way to represent features of miRNAdisease pairs. To our best knowledge, most existing methods heavily rely on biological features of miRNAs and diseases, such as miRNAfamily association, miRNAfunctional similarity and disease semantic similarity. Besides, the features learned from the miRNAdisease association network can be taken into account. Features from known miRNAdisease bipartite network are seldom considered, but they are effective for preserving the property of the network. Therefore, there are three types of feature vectors for describing miRNAdisease pairs, i.e. vectors based on miRNAfamily associations, vectors based on disease semantic similarity and vectors based on Structural Deep Network Embedding.
Representing miRNAs with miRNAfamily associations
There is an assumption that miRNAs in the same family may perform similar biological functions. Here, we utilize miRNAfamily associations to represent miRNA biological feature. For miRNAs M = {M_{1}, M_{2}, …, M_{m}}, families F = {F_{1}, F_{2}, ⋯, F_{t}}, and their associations, we can formulate them as a bipartite network, which uses miRNAs and families as nodes and uses their associations as edges. The bipartite network can be represented by a m × t adjacency matrix Z. This is, Z_{ij} = 1 if miRNA M_{i} belongs to family F_{j}; otherwise, Z_{ij} = 0. Then, for a specific miRNA M_{i}, we use the ith row vector of Z, namely Z_{i, :} to denote its biological feature.
Representing diseases with disease semantic similarity
Inspired by previous works [12], diseases and their relationships can be transformed into a directed acyclic graph (DAG), and DAGs can be used to calculate disease semantic similarity.
For a given disease D, the directed acyclic graph DAG_{D}= (V_{D}, E_{D}). V_{D} denotes the node set including D and other diseases which have relationships with D, and E_{D} denotes the edge set which contains the links from parent disease to child disease. According to DAG, the semantic contribution of disease d in DAG_{D} to disease D can be denoted as:
Here, we set Δ_{∗} = 0.5. The semantic value of disease D can be calculated as:
The semantic similarity between disease D_{i} and disease D_{j} is calculated by:
where \( {S}_{D_i}(d) \) is the semantic contribution of d to disease D_{i}, and \( {SV}_{D_i} \) is the semantic value of D_{i}; for D_{j}, the meanings of \( {S}_{D_j}(d) \) and \( {SV}_{D_j} \) are similar to D_{i}. Then, the semantic similarity between all the diseases can be represented as a n × n matrix SS, and the value in row i and column j of SS represents the disease semantic similarity between D_{i} and D_{j}. For a specific disease D_{i}, we use the ith row vector of SS, namely SS_{i, :} to denote its biological feature.
Representing miRNAdisease pairs with structural deep network embedding
Recently, the network embedding methods show the great potentials of analyzing networks, especially extracting node features. Compared with traditional network analysis methods, which calculate network density, degree statistics and clustering coefficient, the network embedding methods generate lowdimensional vectors that reflect the comprehensive characteristic of networks. Since known miRNAdisease associations could be formulated as a miRNAdisease association network, we naturally use the network embedding methods to extract the features from it. We consider several popular network embedding methods in this work, and compare them in the Comparison with other network Embeddings and other classifiers Section. Because the Structural Deep Network Embedding method performs best among all embedding methods, it is finally adopted for the miRNAdisease association prediction.
Structural Deep Network Embedding method, namely SDNE, is semisupervised deep model, which has multiple layers of nonlinear functions to capture the highly nonlinear network structure through firstorder and secondorder proximity. Since SDNE jointly optimizes firstorder and secondorder proximity, SDNE is robust to sparse networks [54], and outperforms popular network embedding methods in many applications, i.e. graph reconstruction, link prediction and visualization [55].
Given a network with N nodes and the adjacency matrix G = (G_{ij}), we introduce how to learn the node embedding representations. SDNE utilizes the traditional deep autoencoder [56], which has two components: encoder and decoder. The encoder consists of multiple nonlinear functions that maps initial representation of each node x_{i} to a lowrank space through K hidden layers, and the lowrank vector is denoted as y_{i}. x_{i} = G_{i, :},where G_{i, :} is the i th row of the adjacency matrix G mentioned in Pipeline of network embeddingbased multiple information integration method Section. The decoder attempts to reconstruct the representation of the node, and the reconstructed vector is denoted by \( {\hat{x}}_i \).
The firstorder proximity is used as the supervised information to preserve the local network structure, and its objective function is as follows:
where y_{i} is the lowrank representation of node i, and y_{j} is the lowrank representation of node j.
The secondorder proximity is used as the unsupervised information to capture the global network structure, and its objective function is as follows:
where ⊙ means the Hadamard product. B is a N × N matrix. B_{ij} = 1, if G_{ij} = 0, else b_{ij} = β, where β is free parameter and β > 1. X = [x_{1}, x_{2}, ⋯, x_{N}]^{T}, \( \hat{X}={\left[{\hat{x}}_1,{\hat{x}}_2,\cdots, {\hat{x}}_N\right]}^T \).
Moreover, a L2norm regularization term is used to prevent overfitting and defined as follows:
where K is the number of hidden layers, W^{k} and \( {\hat{W}}^{(k)} \) are the kthlayer weight matrices.
SDNE combines Eqs. 1, 2 and 3, and minimizes the following objective function:
More details about SDNE are available in [54].
We can apply SDNE to the miRNAdisease bipartite network with the adjacency matrix G, and obtain a (m + n) × d embedding matrix NE, where d is a free parameter that denotes the dimension of node embeddings. m and n are mentioned in Pipeline of network embeddingbased multiple information integration method Section. The rows of NE, namely NE_{i, :} correspond to the embeddings of m miRNA node and n disease nodes.
We also consider other popular network embedding methods Laplacian Eigenmaps (LE) [23], HighOrder Proximity preserved Embedding (HOPE) [24] and DeepWalk [18], and compare SDNE with them in Comparison with other network Embeddings and other classifiers Section.
Model construction
We combine three types of features to describe miRNAdisease pairs, and then use them to build classificationbased models. Specifically, four feature vectors: miRNAfamily feature vectors, disease semantic similarity feature vectors, miRNA embedding feature vectors and disease embedding feature vectors are merged. We adopt random forest as the classification engine to classify miRNAdisease pairs. Random forest is an ensemble learning method containing multiple classification trees [57]. Each tree is constructed by using a bootstrap sample of the training dataset. For each node within each tree, a randomly selected subset of the input features is used. Then the classification output of random forest is determined by the majority classification of all the trees. Random forest is wellknown for its ability to deal with unbalanced datasets [58], and studies also demonstrated that random forest has good performances for bioinformatics problems [22, 59].
To the best of our knowledge, there are a great number of popular classifiers in bioinformatics, such as logistic regression, naive Bayes and support vector machine. We also compare random forest with these classifiers in Comparison with other network Embeddings and other classifiers Section.
Availability of data and materials
The datasets generated and/or analysed during this study are available under open licenses in the data repository, https://github.com/BioMedicalBigDataMiningLab/NEMII.
Abbreviations
 AUC:

Area under the receiveroperating characteristic curve
 AUPR:

Area under the precisionrecall curve
 DAG:

Directed acyclic graph
 dbDEMC:

Database of differentially expressed miRNAs in human cancers
 DSS:

Disease semantic similarity
 HMDD:

Human microRNA disease database
 HOPE:

HighOrder Proximity preserved Embedding
 LE:

Laplacian Eigenmaps
 LR:

Logistic regression
 MeSH:

Medical subject heading
 miR2Disease:

Database for microRNA deregulation in human disease
 NB:

Naive Bayes
 NEMII:

Network Embeddingbased Multiple Information Integration method
 RF:

Random forest
 SDNE:

Structural Deep Network Embedding
 SVD:

Singular Value Decomposition
 SVM:

Support vector machine
References
 1.
Ribeiro AO, Schoof CR, Izzotti A, Pereira LV, Vasques LR. MicroRNAs: modulators of cell identity, and their applications in tissue engineering. Microrna. 2014;3(1):45–53.
 2.
Vasudevan S, Tong Y, Steitz JA. Switching from repression to activation: MicroRNAs can upregulate translation. Science. 2007;318(5858):1931–4.
 3.
Xantha K, Victor A. Developmental biology. Encountering microRNAs in cell fate signaling. Science. 2005;310(5752):1288.
 4.
Miska EA. How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev. 2005;15(5):563–8.
 5.
Cheng AM, Byrom MW, Shelton J, Ford LP. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 2005;33(4):1290–7.
 6.
Xu P, Guo M, Hay BA. MicroRNAs and the regulation of cell death. Trends Genet. 2004;20(12):617–24.
 7.
Ming L, Qipeng Z, Min D, Jing M, Yanhong G, Wei G, Qinghua C. An analysis of human microRNA and disease associations. PLoS One. 2008;3(10):e3420.
 8.
Iorio MV, Manuela F, ChangGong L, Angelo V, Riccardo S, Silvia S, Eros M, Massimo P, Muller F, Manuela C. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65(16):7065.
 9.
Latronico MV, Catalucci D, Condorelli G. Emerging role of microRNAs in cardiovascular biology. Circ Res. 2007;101(12):1225–36.
 10.
LynamLennon N, Maher SG, Reynolds JV. The roles of microRNA in cancer and apoptosis. Biol Rev Camb Philos Soc. 2010;84(1):55–71.
 11.
Sun D, Li A, Feng H, Wang M. NTSMDA: prediction of miRNAdisease associations by integrating network topological similarity. Mol BioSyst. 2016;12(7):2224.
 12.
You ZH, Huang ZA, Zhu Z, Yan GY, Li ZW, Wen Z, Chen X. PBMDA: a novel and effective pathbased computational model for miRNAdisease association prediction. PLoS Comput Biol. 2017;13(3):e1005455.
 13.
Chen X, Wu QF, Yan GY. RKNNMDA: rankingbased KNN for MiRNAdisease association prediction. RNA Biol. 2017;14(7):1.
 14.
Xiao Q, Luo J, Liang C, Cai J, Ding P. A graph regularized nonnegative matrix factorization method for identifying microRNAdisease associations. Bioinformatics. 2017;34(2):239–48.
 15.
Chen X, Wang L, Qu J, Guan NN, Li JQ. Predicting miRNAdisease association based on inductive matrix completion. Bioinformatics. 2018;34(24):4256–65.
 16.
Luo J, Ding P, Liang C, Chen X. Semisupervised prediction of human miRNAdisease association based on graph regularization framework in heterogeneous networks. Neurocomputing. 2018;294:29–38.
 17.
Chen X, Xie D, Wang L, Zhao Q, You ZH, Liu H. BNPMDA: bipartite network projection for MiRNAdisease association prediction. Bioinformatics. 2018,34(18):3178–3186.
 18.
Perozzi B, AlRfou R, Skiena S. DeepWalk: Online Learning of Social Representations; 2014. p. 701–10.
 19.
Aditya Grover JL: node2vec: Scalable Feature Learning for Networks. In: Acm Sigkdd International Conference on Knowledge Discovery & Data Mining; 2016. p. 855–864.
 20.
Zong N, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drugtarget associations. Bioinformatics. 2017;33(15):2337–2344.
 21.
Li G, Luo J, Xiao Q, Liang C, Ding P, Cao B. Predicting MicroRNAdisease associations using network topological similarity based on DeepWalk. IEEE Access. 2017;5:24032–24039.
 22.
Liu X, Yang Z, Sang S, Zhou Z, Wang L, Zhang Y, Lin H, Wang J, Xu B. Identifying protein complexes based on node embeddings obtained from proteinprotein interaction networks. Bmc Bioinformatics. 2018;19(1):332.
 23.
Belkin M, Niyogi P. Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computation. 2003;15(6):1373–1396.
 24.
Ou M, Cui P, Pei J, Zhang Z, Zhu W. Asymmetric transitivity preserving graph embedding. In: The ACM SIGKDD International Conference; 2016. p. 1105–14.
 25.
Wang D, Cui P, Zhu W. Structural deep network embedding. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016. p. 1225–34.
 26.
Fassan M, Saraggi D, Balsamo L, Cascione L, Castoro C, Coati I, Bernard MD, Farinati F, Guzzardo V, Valeri N. Let7c downregulation in helicobacter pylori related gastric carcinogenesis. Oncotarget. 2016;7(4):4915–24.
 27.
Aslan D, Garde C, Nygaard MK, Helbo AS, Dimopoulos K, Hansen JW, Severinsen MT, Treppendahl MB, Sjø LD, Grønbæk K. Tumor suppressor microRNAs are downregulated in myelodysplastic syndrome with spliceosome mutations. Oncotarget. 2016;7(9):9951–63.
 28.
Kazuhiko B, Kumar S, Rachel S, Pranavkumar S, Reena M, Stephanie W, Vivek K, Eric D, Jegga AG, Bezerra JA. Integrative genomics identifies candidate microRNAs for pathogenesis of experimental biliary atresia. BMC Syst Biol. 2013;7(1):104.
 29.
Li WJ, Xie XX, Bai J, Wang C, Zhao L, Jiang DQ. Increased expression of miR1179 inhibits breast cancer cell metastasis by modulating notch signaling pathway and correlates with favorable prognosis. Eur Rev Med Pharmacol Sci. 2018;22(23):8374–82.
 30.
Merino MJ, Gil S, Macias CG, Lara K. The unknown microRNA expression of male breast cancer. Similarities and differences with female ductal carcinoma. Their role as tumor biomarker. J Cancer. 2018;9(3):450–9.
 31.
Boya X, Qin D, Hongjin H, Di W. miRCancer: a microRNAcancer association database constructed by text mining on literature. Bioinformatics. 2013;29(5):638–44.
 32.
Phan B, Majid S, Ursu S, Semir DD, Nosrati M, Bezrookove V, KashaniSabet M, Dar AA. Tumor suppressor role of microRNA1296 in triplenegative breast cancer. Oncotarget. 2016;7(15):19519–30.
 33.
Hu JY, Yi W, Zhang MY, Xu R, Zeng LS, Long XR, Zhou XM, Zheng XS, Kang Y, Wang HY. MicroRNA711 is a prognostic factor for poor overall survival and has an oncogenic role in breast cancer. Oncol Lett. 2016;11(3):2155–63.
 34.
Song L, Dai Z, Zhang S, Zhang H, Liu C, Ma X, Liu D, Zan Y, Yin X. MicroRNA1179 suppresses cell growth and invasion by targeting spermassociated antigen 5mediated Akt signaling in human nonsmall cell lung cancer. Biochem Biophys Res Commun. 2018;504(1):164–170.
 35.
Jiang W, Tian Y, Jiang S, Liu S, Zhao X, Tian D. MicroRNA376c suppresses nonsmallcell lung cancer cell growth and invasion by targeting LRH1mediated Wnt signaling pathway. Biochem Biophys Res Commun. 2016;473(4):980–6.
 36.
Hu S, Yuan Y, Song Z, Yan D, Kong X. Expression profiles of microRNAs in drugresistant nonsmall cell lung Cancer cell lines using microRNA sequencing. Cell Physiol Biochem. 2018;51(6):2509–22.
 37.
Chaohui W, Yunpeng C, Zefeng H, Jianbing H, Chao H, Hongbing D, Jie J. Serum levels of miR19b and miR146a as prognostic biomarkers for nonsmall cell lung cancer. Tohoku J Exp Med. 2014;232(2):85–95.
 38.
Mohan RD, Bibber B, Sinha G, Patel SA, Rameshwar P. MicroRNA in development and in the progression of cancer; 2014.
 39.
Moustafa AA, Ziada M, Elshaikh A, Datta A, Kim H, Moroz K, Srivastav S, Thomas R, Silberstein JL, Moparty K, et al. Identification of microRNA signature and potential pathway targets in prostate cancer. Exp Biol Med. 2017;242(5):536–46.
 40.
Stuopelyte K, Daniunaite K, Jankevicius F, Jarmalaite S. Detection of miRNAs in urine of prostate cancer patients. Medicina. 2016;52(2):116–24.
 41.
Ping X, Ke H, Maozu G, Yahong G, Jinbao L, Jian D, Yong L, Qiguo D, Jin L, Zhixia T et al: The top 50 prostatic neoplasmsrelated miRNA candidates; 2013.
 42.
Yang L, Chengxiang Q, Jian T, Bin G, Jichun Y, Tianzi J, Qinghua C. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014;42(Database issue):D1070.
 43.
Yang Z, Ren F, Liu C, He S, Sun G, Gao Q, Yao L, Zhang Y, Miao R, Cao Y. dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics. 2010;11(Suppl 4):1–8.
 44.
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(1):D98–104.
 45.
Li Y, Qiu C, Tu J, Geng B, Yang J, Jiang T, Cui Q. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014;42(Database issue):D1070.
 46.
Kozomara A, GriffithsJones S. miRBase: integrating microRNA annotation and deepsequencing data. Nucleic Acids Res. 2011;39(suppl_1):D152–7.
 47.
Wen Z, Liu X, Chen Y, Wu W, Li X. Featurederived graph regularized matrix factorization for predicting drug side effects. Neurocomputing. 2018;287:154–162.
 48.
Wen Z, Xiang Y, Feng H, Ruoqi L, Yanlin C, Chunyang R. Predicting drugdisease associations and their therapeutic function based on the drugdisease association bipartite network. Methods. 2018;145:51–59.
 49.
Wen Z, Xiang Y, Weiran L, Wenjian W, Ruoqi L, Feng H, Feng L. Predicting drugdisease associations by using similarity constrained matrix factorization. Bmc Bioinformatics. 2018;19(1):233.
 50.
Zhang W, Chen Y, Li D, Yue X. Manifold regularized matrix factorization for drugdrug interaction prediction. J Biomed Inform. 2018;88:90–97.
 51.
Zhang W, Jing K, Huang F, Chen Y, Li B, Li J, Gong J. SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions. Inf Sci. 2019;497:189–201.
 52.
Zhang W, Yu C, Wang X, Liu F. Predicting CircRNAdisease associations through linear neighborhood label propagation method. IEEE Access. 2019;7:83474–83.
 53.
Zhang W, Yue X, Tang G, Wu W, Huang F, Zhang X, Ioshikhes I. SFPELLPI: sequencebased feature projection ensemble learning for predicting LncRNAprotein interactions. PLoS Comput Biol. 2018;14(12):e1006616.
 54.
Wang D, Cui P, Zhu W. Structural Deep Network Embedding; 2016. p. 1225–34.
 55.
Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: a survey. KnowlBased Syst. 2018;151:78–94.
 56.
Elsevier. International Journal of Approximate Reasoning. Mathware Soft Comput. 2012;53(1):17–29.
 57.
Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
 58.
Chen C, Breiman L. Using random forest to learn imbalanced data; 2004.
 59.
Taherzadeh G, Zhou Y, Liew AW, Yang Y. Structurebased prediction of protein peptide binding regions using random Forest. Bioinformatics. 2018;34(3):477–84.
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This work is supported by the National Natural Science Foundation of China (61772381, 61572368), the Fundamental Research Funds for the Central Universities (2042017kf0219, 2042018kf0249), National Key Research and Development Program (2018YFC0407904), Huazhong Agricultural University Scientific & Technological Selfinnovation Foundation. The funders have no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.
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WZ conceived the project; YG and WZ designed the experiments; YG performed the experiments; YG, YN, WZ and XL wrote the paper. All authors read and approved the final manuscript.
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Correspondence to Wen Zhang or Xiaohong Li.
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Figure S1. Fivefold crossvalidation (CV) for Network Embeddingbased Multiple Information Integration Method. (PDF 312 kb)
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Gong, Y., Niu, Y., Zhang, W. et al. A network embeddingbased multiple information integration method for the MiRNAdisease association prediction. BMC Bioinformatics 20, 468 (2019) doi:10.1186/s1285901930633
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Keywords
 miRNAdisease associations
 Network embedding
 Random forest