Long non-coding RNAs (lncRNA) are RNA transcripts with more than 200 nucleotides in length and lack of protein-coding potential Goff and Rinn (2015). The studies revealed that lncRNAs were involved as a regulator in many biological processes such as ageing, cell differentiation, epigenetic mechanisms and protein synthesis Lee (2012). The aberrant expression of lncRNAs is also associated with many complex diseases like cancers Huarte (2015), alzheimer’s disease Luo and Chen (2016), and heart failure Wang et al. (2019). Therefore, identifying the role of lncRNAs in diseases will help to improve the understanding of the disease mechanisms and derive new insights on drug therapeutics Bhartiya et al. (2012).
An array of computational models to predict the lncRNA-disease association are available in the literature with varying performance. All these works utilise varieties of lncRNA features from known lncRNA-disease associations and their interactions with other molecules like micorRNAs (miRNA), proteins and messengerRNAs (mRNA). There are three major categories of works in lncRNA-disease association. The first category of works utilise the knowledge of lncRNA functional similarity under the assumption that functionally similar lncRNAs associate with similar diseases. Based on this assumption, an lncRNA-disease association network was constructed. Then, algorithms from machine learning and social network analysis were used to make the lncRNA-disease prediction. For example, methods such as RWRlncD Sun et al. (2014), IRWRLDA Chen et al. (2016), BRWLDA Yu et al. (2017) used various random walk algorithm and KATZLDA Chen (2015) applied Katz page ranking algorithm for analysing the similarity network. All these methods relied upon the network structure features and results were biased towards nodes having high degree and centrality.
The advanced research on lncRNA mechanism revealed that the regulation of lncRNA is largely determined by co-expressed miRNAs Paraskevopoulou and Hatzigeorgiou (2016). The second category of works used expression levels of lncRNAs, genes and miRNAs in various diseases. The earlier methods Yao et al. (2020) in this category used experimentally validated disease associated genes/miRNAs and lncRNA co-expression data. These methods were not useful with lncRNAs which have no experimentally validated gene/miRNA interaction. Recent works under this category used mathematical models such as matrix completion Lu et al. (2018), matrix factorization Fu et al. (2018) and Graph-based algorithms (TPGLDA Ding et al. (2018), DisLncPri Wang et al. (2017)) to predict lncRNA-disease association.
The third category of works constructed a heterogeneous interaction network based on lncRNA, miRNA and mRNA functional similarity and associations. Machine learning techniques such as Random Walk Yao et al. (2020)
, Support Vector Machine (SVM)Lan et al. (2017), and Laplacian regularised least square method Chen et al. (2015)
have found use in the analysis of these networks. Major challenge in the above methods was the effective representation of lncRNA-disease features. Introduction of deep learning models eliminated the need for feature extraction by enabling unsupervised representation learning. Among them, CNNLDAXuan et al. (2019a)
used convolutional neural networks and GCNLDAXuan et al. (2019b) used graph convolutional neural networks Kipf and Welling (2016) to learn global representations of lncRNA, miRNA and disease nodes. A recent work titled GAMCLDA Wu et al. (2020)
used graph autoencoder and matrix completion to predict lncRNA-disease association.
Deep learning algorithms such as Deep Belief Networks (DBN) were used recently in computer vision and text mining to learn the latent representation of the dataHinton (2009)
. A DBN model has stacked layers of Restricted Boltzman Machines (RBM), which contains visible layers and hidden layers to compute probability distribution as latent representation. DBN based models were successfully used in Bioinformatics to predict drug-targetWang and Zeng (2013), multiple types of miRNA-disease association Chen et al. (2015); Luo et al. (2019) and cancer sub-type prediction Liang et al. (2014), but not applied in the field of lncRNA-disease association prediction.
In this study, we propose a DBN based representation learning model (called DBNLDA) for lncRNA-disease association prediction. DBNLDA make use of heterogeneous information on functional similarity, co-expression and interactions between lncRNAs and diseases for making the prediction. The architecture of DBNLDA contains 3 modules namely-(i)network construction, (ii) DBN based feature learning and (iii) association prediction. Summary of the proposed architecture is depicted in Figure 1. Instead of using a single heterogeneous network of lncRNAs, miRNAs and diseases as in previous works Yao et al. (2020); Xuan et al. (2019b), DBNLDA constructs three functional similarity networks such as lncRNA-miRNA (LMS), disease-miRNA (DMS) and lncRNA-Disease association (LDA). Then, for each pair of lncRNA-disease, DBN subnetwork-1 learns lncRNA and disease representations from their functional associations with miRNAs. Similarly, DBN subnetwork-2 learns lncRNA-disease representations from LDA network. The representations learned by subnetworks are combined for learning higher-level representation using the third DBN (DBN-combined). In order to reduce the obstructive impact of features learned from sparse network, the features from DBN-combined are recomputed using an attention layer. In the final stage, a neural network-based classifier is used to predict the lncRNA-disease association based on the joint feature representation. A 5-fold cross-validation and case studies on cancer dataset showed that DBNLDA significantly improves the performance and potential lncRNA-disease association prediction.
2 Materials and Methods
2.1 The Dataset
Datasets used in the previous reports Yao et al. (2020); Xuan et al. (2019b); Fu et al. (2018) were used for lncRNA-Disease associations, lncRNA-miRNA interaction, lncRNA function similarity and disease semantic similarity. LncRNA-disease associations were downloaded from two reference databases: LncRNADisease Chen et al. (2012) and lnc2cancer Ning et al. (2016). MiRNA-lncRNA interactions and miRNA-disease interactions were downloaded from miRNet Fan et al. (2016) and Starbase Li et al. (2014) databases respectively. All these downloaded associations and similarities were then compiled for 240 lncRNAs, 412 diseases, 495 miRNAs and 2697 known lncRNA- disease interactions. These known interaction pairs constituted the positive example for training the model. All other pairs between lncRNAs and diseases, which were not listed in reference databases, were considered to be negative interactions. We randomly selected 2967 samples from negative interactions to construct a balanced dataset. Summary of the dataset is given in Table 1.
2.2 Construction of LMS, DMS and LDA networks
The first step in the DBNLDA architecture was the construction of three similarity networks such as LMS, DMS and LDA as defined in Section 1. Let and be the number of lncRNAs, miRNAs and diseases in the dataset. LMS network (with lncRNAs and miRNAs) was constructed using lncRNA-lncRNA similarity and lncRNA-miRNA interactions. LncRNA functional similarities computed by Chen’s method Chen et al. (2015) was adapted here, and an edge was added if the similarity score was greater than 0. For the list of lncRNAs, the known miRNA targets downloaded from the miRNet were used as lncRNA-miRNA edges.
Similar to LMS, DMS network was constructed (with diseases and miRNAs) using disease-disease similarities and known disease-miRNA associations. LncRNA-Disease association (LDA) network contains lncRNAs and diseases, where an un-directed edge was used to represent the known association between lncRNA and disease.
2.3 Computing node embedding features from networks
Node2vec Grover and Leskovec (2016) algorithm was used to get node embedding from each network. Node2vec output a node representation based on the neighbourhood information of the node. First, LMS network was passed through node2vec embedding layer, and embedding matrix for lncRNA was obtained as , where is the embedding dimension. The embedding matrix for diseases from DMS was obtained as . Then, for each lncRNA-disease pair in the dataset (both positive and negative examples), and were concatenated to form vector . This resulted in a feature matrix , where is the total number of examples in the dataset. Finally, the embedding matrices of lncRNAs and diseases, represented as , were obtained from LDA network. Then as in the case of , for each pairs of lncRNA-disease, vectors from and were concatenated to form feature matrix .
2.4 DBN based feature learning
Following the Node2vec embedding layer, DBNLDA implements Deep Belief Network based feature learning. In this work, DBN was used to learn latent representation of lncRNA and disease nodes and encode them to a new dimension , where . The architecture consisted of two DBN subnetworks- one to learn lncRNA-disease representation from functional similarity networks (LMS and DMS) and other from LDA network. The DBN subnetwork-1 received embedded feature matrix as input, and produce (, the number of hidden units in DBN) as output. Similarly, the DBN subnetwork-2 received as input and produce as output. Then both and were concatenated to form combined feature representation, . The network DBN-combined accepted as input and learn the feature representation as , where is the number of hidden units in the DBN-combined and .
2.5 Feature attention layer
Since all the three networks used in the DBNLDA architecture were sparse in nature, there was a chance of losing feature importance in the latent representation learned by DBN. The attention mechanism in deep learning was used to solve this issue by recomputing the feature values from all available information so that their contributions could be different and unique. In this work, attention mechanism similar to the one used in GCNLDA Xuan et al. (2019b) was applied to features.
Let represents the feature vector of entry in the dataset learned by DBN-combined, where . The attention score for each element in was calculated by introducing attention weight parameters , and bias as in Equation 1.
Next, the attention enhanced feature values were recomputed as in Equation 2.
where represents pairwise multiplication. Finally, the matrix was used as input for association prediction.
2.6 Prediction Layer
The final module of DBNLDA architecture is a neural network classifier to predict the association between lncRNA and disease. The classifier network followed a 1-2-1 structure, where the input layer has nodes to receive input from
and output layer has one node to predict the association score. The number of nodes in hidden layers kept as hyperparameter. Activation function used in all layers except the output was Relu. In order to reduce the overfitting, a dropout of 0.02 probability was added between the hidden layers. Output layer used a sigmoid function to compute the association score. The network used ADAM optimizer with learning rate 0.01 and binary cross-entropy loss function.
Various hyperparameters determine the performance of DBNLDA in different modules. The values of these hyperparameters were set empirically following performance evaluation. The dimension of node2vec embedding, is set to be 64 for all networks with keeping other parameters to default values in Grover and Leskovec (2016). Following the implementation of Luo et al. (2019), the DBN subnetworks in this work uses three stacked layers of RBM, with nodes in all layers. The combined DBN has dimension . It is found that very low values and degrade the performance and high values have no effect on the performance (see Figure 2epoch.
A detailed description DBNLDA steps and implementation details are available in SupplementaryFile-S1.pdf.
3 Results and Discussion
3.1 Performance evaluation metrics
We used 5-fold cross-validation to measure and compare the performance of the model. The dataset consist of 5394 lncRNA-disease pairs (2697 positive associations and 2697 negative associations). The area under the receiver operating characteristic (AUC-ROC) curve was used to compare the global performance of the prediction model. The area under the precision-recall curve (AUPR) and average accuracy were also used to measure the prediction performance.
3.2 Overall performance
The DBNLDA was trained over 30 epochs and the learning curve (refer Figure 3) showed consistent characteristics in all folds of cross validation. The model gave average AUC of 0.96 over cross validations and ROC curve is shown in Figure 4. The model reported an accuracy of 0.957 and AUPR value of 0.968.
In order to analyse how DBN based features improve the performance of the prediction model, we have repeated the experiments with different levels of feature combinations. It is clear from the Table 2 that introduction of DBN based learning significantly improved the accuracy of the prediction model.
|Exp 1||only Node2vec featues||0.817|
|Exp 2||Node2vec, DBN subnetwork-1 and||0.896|
|Exp 3||Node2vec, DBN subnetwork-1,||0.956|
|DBN subnetwork-2 and DBN-combined|
3.3 Comparison with other methods
We compared the performance of DBNLDA with other state-of-the-art methods such as RFLDA Yao et al. (2020), GCNLDA Xuan et al. (2019b), SIMCLDALu et al. (2018), Ping’s Method Ping et al. (2018), MFLDA Fu et al. (2018), LDAP Lan et al. (2017), GAMCLDA Wu et al. (2020) and CNNLDA Xuan et al. (2019a), based on area under ROC curve (AUC) and Area under Precision-Recall curve (AUPR). The above methods used knowledge from heterogeneous information from different data sources to predict lncRNA-disease association and not consider network structure features. AUC and AUPR values of all LDA prediction models were given in Table 3. The AUC and AUPR values of all methods except DBNLDA were taken from Yao et al. (2020) and Wu et al. (2020). It was evident from the table that DBNLDA reported second best AUC (0.96) which is closer (1.6% less) to the highest AUC value reported by RFLDA. Moreover, DBNLDA outperforms all other methods in terms of AUPR values, where DBNLDA have 0.968 which is 18.9% better than the second highest value (RFLDA). These results show that DBNLDA predicts lncRNA-disease associations effectively.
|MFLDA Fu et al. (2018)||0.626||0.066|
|SIMCLDA Lu et al. (2018)||0.746||0.095|
|LDAP Lan et al. (2017)||0.863||0.166|
|Ping’s Method Ping et al. (2018)||0.871||0.219|
|GAMCLDA Wu et al. (2020)||0.907||0.037|
|CNNLDA Xuan et al. (2019a)||0.952||0.251|
|GCNLDA Xuan et al. (2019b)||0.959||0.223|
|RFLDA Yao et al. (2020)||0.976||0.779|
3.4 Case studies
|HCP5||3||Literature (PMID: 31864836) Wu et al. (2019)|
|MIR100HG||14||Literature (PMID:30042378) Wang et al. (2018)|
|TUG1||1||Literature (PMID:31532756) Guo et al. (2019)|
|XIST||4||Literature (PMID: 28448993) Wang et al. (2017)|
|HULC||9||Literature (PMID:30575912) Liu et al. (2018)|
|MIR155HG||10||Literature (PMID:32129458) Song et al. (2020)|
|CDKN2B-AS1||11||Literature (PMID:29541247) Du et al. (2018)|
|MIR17HG||1||Literature (PMID:26837962) Bahari et al. (2015)|
|BCYRN1||2||Literature (PMID:29435146) Ren et al. (2018)|
|NALT1||7||Literature (PMID:31802831) Piao et al. (2019)|
To further investigate the ability of DBNLDA in predicting significant lncRNA-disease associations, case studies on breast cancer, lung cancer and stomach cancer were conducted. For this study, first trained the DBNLDA model on a dataset containing all lncRNA-disease associations except the validated association between lncRNAs and the disease of interest (breast/lung/stomach cancer). Then the association score for all lncRNAs to the particular disease was calculated using the trained model and analysed the top 15 candidate lncRNAs for each disease. Tables 4,5, and 6 show the top 15 candidate lncRNAs in breast/lung/stomach cancer, predicted by DBNLDA. The evidence column shows the reference to the associations either from reference databases or literature.
It was found that 13 (86.67%) lncRNAs associated with breast cancer predicted by DBNLDA were also confirmed by lnc2cancer or LncRNADisease database. For the two unconfirmed predictions, we could find the evidence from recent publications. In the case of lung cancer, among the top 15 predicted lncRNAs, 10 were confirmed by reference databases and remaining were reported in recent literature. In case of stomach cancer, DBNLDA could predict 12 associations reported either by reference databases or literature. The three new associations (HCP5, HCG4 and MIR99AHG, indicated by ’*’ in Table 6) which could be considered as new suggestions for further laboratory validations. The detailed comparison of LDA prediction by DBNLDA is available in SupplementaryTable-S2.xls.
Identifying disease-associated lncRNA is a necessary step in discerning the functional roles of lncRNAs in the disease mechanism. In this work, we developed DBNLDA, a deep belief network-based model for lncRNA-disease association prediction. This work integrates information of lncRNA, miRNA, disease interactions, and functional similarities to construct heterogeneous networks. Then DBN based latent representations of lncRNAs and diseases are used to predict the lncRNA-disease association accurately. The cross-validation confirmed that DBNLDA has comparable performance in terms of AUC and significant improvement in terms of AUPR. Case studies on breast cancer, lung cancer and stomach cancer show the ability of DBNLDA to predict potential disease-associated lncRNAs. The model could be extended further with multi-modal data such as lncRNA drug-target interactions and lncRNA-epigenetic-disease interactions.
This research work is an outcome of the R& D work under the Visvesvaraya PhD Scheme of Ministry of Electronics and Information Technology, Government of India
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