 Research
 Open Access
 Published:
Discovering functional impacts of miRNAs in cancers using a causal deep learning model
BMC Medical Genomics volume 11, Article number: 116 (2018)
Abstract
Background
MicroRNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA in a data driven fashion. On one hand, sequencebased methods for predicting miRNA targets tend to make too many false positive calls. On the other hand, analyzing expression correlation between miRNAs and mRNAs cannot establish whether relationship between a pair of correlated miRNA and mRNA is causal.
Methods
In this study, we designed a deep learning model, referred to as miRNA causal deep net (mCADET), which aims to explicitly represent two types of statistical relationships between miRNAs and mRNAs: correlation resulting from confounded coregulation and correlation as a result of causal regulation. The model utilizes a deep neural network to simulate transcription mechanism that leads to coexpression of miRNA and mRNA, and, in addition, it also contains directed edges from miRNAs to mRNAs to capture causal relationships among them.
Results
We trained the mCADET model using pancancer miRNA and mRNA data from The Cancer Genome Atlas (TCGA) project to investigate mechanism of coexpression and causal interactions between miRNAs and mRNAs. Quantitative analyses of the results indicate that the mCADET significantly outperforms conventional deep learning models when modeling combined miRNA and mRNA expression data, indicating its superior capability of capturing the highorder statistical structures in the data. Qualitative analysis of predicted targets of miRNAs indicate that predictions by mCADET agree well with existing knowledge. Finally, the predictions by mCADET have a significantly lower false discovery rate and better overall accuracy in comparison to sequencebased and correlationbased methods when comparing to experimental results.
Conclusion
The mCADET model can simultaneously infer the states of cellular signaling system regulating coexpression of miRNAs and mRNAs, while capturing their causal relationships in a datadriven fashion.
Background
Regulated expression of miRNAs and their regulatory functions
MicroRNAs are a class of small RNAs, about 22 nucleotides in length and involved in posttranscriptional and translation regulation of geneexpression either by direct cleavage of mRNA or translational repression [1]. In the last decade, studies show that the dysfunction/dysregulation of certain miRNAs are involved in the development and progression of many diseases. Particularly, the role of miRNA in cancer has drawn attention in last decade. Studies demonstrated that the dysregulation of miRNA expression could lead to human cancers [2]. The mechanisms include amplification or deletion of miRNA genes [3], abnormal transcriptional control of miRNAs [4], dysregulated epigenetic changes [5, 6] and deficiencies in the miRNA biogenesis machinery [7]. The loss of tumor suppressor miRNAs or overexpression of oncogenic miRNAs can lead to breast cancer tumorigenesis or metastasis [8, 9].
To gain a better understanding of the roles of miRNAs in normal biological processes and in the development of disease, it is important to accurately identify which genes are targeted by each miRNA. Since it is infeasible to perform biological experiments for such a large number of miRNAs, computational methods play an important role in studying miRNA, and numerous computational methods have been developed for predicting targets of miRNAs. To predict the interaction between miRNA and mRNA, many tools have been developed using different algorithms [10,11,12,13,14], although two main approaches dominate the field. One approach is the sequencebased miRNA target prediction, which models the complementary sequence similarity between miRNA and mRNA and structural stability of the putative duplex to predict whether a mRNA is a target of a miRNA [12, 15,16,17]. Give a miRNA sequence dataset (e.g., miRBase [18], StarBase [11]), sequencebased models can be used to scan whole mRNA transcriptome to predict potential targets. However, these methods have been shown to have a high rate of false positives and false negatives [19]. This is mainly because sequence similarity is not sufficient to predict the folding of RNA duplexes and their interaction with the proteins involved in miRNAmediated regulation [19, 20].
Another common approach of studying miRNA and mRNA relationship is the correlationbased miRNA target prediction. Based on miRNA and mRNA expression data collected from a cohort of biological samples, correlationbased methods search for anticorrelation relationships between pairs of miRNA and mRNA as potential regulatortarget pairs. Different databases based on correlation analyses are available, e.g., mirCox [21]. However, an anticorrelation between a pair of miRNA and mRNA does not necessarily represent a causal relationship. It is not uncommon that a signaling pathway may simultaneously regulate expression of a miRNA and a set of mRNAs, which may lead to confounded correlation. As it is often said: correlation does not entail causality. Thus more rigorous causal inference methods are needed to infer the causal relationships between miRNA and mRNA.
In this study, we present a novel method of studying the statistical relationships between mRNAs and miRNAs by analyzing largescale data from TCGA. Our method integrates two complementary machine learning frameworks: deep learning and causal inference. Our model, referred to as miRNA causal deep net (mCADET), consists of deep neural network that can capture the transcriptomic machine that controls expression of both miRNA and mRNAs to capture the statistical structure resulting from coregulation, and, in addition, it includes directed edges from miRNA to mRNA to capture the potential causal relationships between miRNA and mRNA. We show that this integrative approach can significantly outperform the sequencebased and correlationbased methods in predicting targets of miRNAs.
Methods
Data collection
The miRNA and mRNA expression data were obtained and downloaded from TCGA consortium website (https://cancergenome.nih.gov/). For the breast cancer (BRCA), 1218 mRNA sequencing samples were downloaded with 20,531 mRNAs, and 1701 miRNA sequencing samples were downloaded with 1918 microRNAs, which includes duplicate and triplicate samples. We further identified tumor samples with both mRNA and miRNA measurements. We discretized (binary) the expression data by comparing the expression values of mRNA and miRNAs from tumor samples with those from normal samples profiled by TCGA using the 3fold change. Finally, we merged the miRNA and mRNA dataset into a 757 ×22,449 (samples × combined miRNA and mRNA) binary matrix for model training.
Several openresource miRNAtarget databases are used in this paper including miRTarBase (an experimentally validated database) [22], and miRDB (a sequence based prediction database and the prediction tool used is MirTarget V3) [23, 24]. TMREC [25] and TTRUST [26] were used to look up the TFmiRNA and TFmRNA interactions separately to help find the common TF regulating both a particular miRNA and a particular mRNA.
Model
Model architecture
As we mentioned above, the high correlation doesn’t guarantee the causality. The correlation between miRNA and mRNA could be a result of two types of regulations: 1) Transcriptions of a miRNA and a mRNA are regulated by a common cellular signal (Fig. 1a), which can be a common pathway or a common transcription factor (TF). In other words, expressions of the miRNA and mRNA are confounded by a common latent variable. 2) Expression of a miRNA and a mRNA are regulated by distinct cellular signals, but miRNA can causally regulate the degradation of mRNA (Fig. 1a). To capture these two types relationships, we designed a hybrid model, which uses a deep autoencoder to capture the signals of cellular signaling systems to explain the coregulation of miRNAs and mRNAs and further includes causal edges from miRNAs to mRNAs to capture the causal relationships. The regulatory type between miRNAs and mRNAs could be reflected by +/− sign of the predicted interaction value.
An autoencoder uses multiple layers of latent variables (hidden nodes) to capture compositional statistical structures in a distributed manner, such that each layer captures the structure of different degrees of abstraction. As shown in Fig. 1b, an autoencoder contains one visible (input) layer and one or more hidden layers. To efficiently train the autoencoder, we treat it as a series of twolayered restricted Boltzmann machines (RBM) stacked on top of each other [27]. The inference of the states of hidden node and learning of model parameters are performed by learning the RBM stacks in a bottomup fashion, which is followed by a global optimization of generative parameters using the backpropagation algorithm [28]. As shown in our previous studies [29, 30], autoencoders are capable of capturing signals regulating gene expressions under different settings. For example, the first hidden layer could represent the transcription factors (TFs).
To further capture the causal relationships between miRNAs and mRNAs, we designed a deep belief network (DBN) model containing directed edges from miRNA to mRNAs. We pretrain the model by treating the observed variables (miRNA and mRNA) and first hidden layer above them as a semiRBM. The semiRBM between the first two layers allows the edges from miRNA visible nodes to mRNA visible nodes to reflect the regulatory role of miRNA in gene expression. The layers above the second layer still use the traditional RBMs as shown in Fig. 1b. The followings show how the positive phase and negative phase perform. Under this setting, when the correlation between a pair of miRNA and mRNA are competently explained by coregulation or causal edges, regularization techniques (discussed later) potentially constrain the model to pick one dominant mechanism to represent the correlation.
SemiRBM positive phase
As shown in Fig. 1c, both miRNA and mRNA contribute to the activation of hidden units in the first hidden layer.
where v_{mrna & mirnai} represents the binary state of ith visible unit of mRNA and miRNA; h_{j} represents the state of jth hidden unit; b_{j} represents the bias for the jth hidden unit; W_{ij} represents the weight between the ith visible unit of mRNA and miRNA and the jth hidden unit.
SemiRBM negative phase
As shown in Fig. 1d, only the hidden units in the first hidden layer contribute to the activation of the mRNA. However, both the hidden units in the first hidden layer and the mRNAs contribute to the activation of miRNAs.
where v_{mirna _ k} represents the kth visible unit of miRNA; W_{kj} represents the weight between the kth visible unit of miRNA and the jth hidden unit; a_{mirna _ k} represents the bias for the kth visible unit of miRNA; v_{mrna _ o} represents the oth visible unit of mRNA; a_{mrna _ o} represents the bias for the oth visible unit of mRNA; W_{oj} represents the weight between oth visible unit of mRNA and the jth hidden unit; π_{ok} represents the weight between oth visible unit of mRNA and the kth visible unit of miRNA. cbind represents the combination of mRNA and miRNA for the same sample.
SemiRBM parameter update
More details of the algorithm and pseudo code for training a traditional autoencoder were discussed in both literature and our previous work [29, 30]. The backpropagation process is the same as the standard one except that we update the visible units of miRNA using the hidden units in the first hidden layer only, but use both hidden units in the first hidden layer and the visible units of miRNA to update the visible units of mRNA.
Regularization
The number of miRNA and mRNA training samples is limited compared with the dimension of miRNA and mRNA features. Therefore, we applied the techniques of regularization to the first two hidden layers to reduce the risk of overfitting. When we train a traditional RBM model, each hidden unit is fully connected to each observed unit and a nonzero weight is usually assigned to each pair of observed unit and hidden unit. However, in the real cases of cellular regulatory mechanism, the change in mRNA and miRNA expression is often induced by a small number of biological components such as TFs or pathways. This enables one to specify that only a certain percent of hidden units have a high probability to be set to 1 (“on”) by adding a penalization term to the optimization function. In this model, regularization was only added to the first hidden layer [31]. During the RBM training within an autoencoder, the optimization of the sparse RBM minimizes the negative loglikelihood of the data with the addition of the regularization term [32].
where θ is the parameter vector [a, b, W]; s is the total number of samples; n is the total number of hidden units; λ is the regularization constant threshold and p is a constant controlling the percent of hidden units h_{j} to be active (the sparseness of the hidden units h_{j}). Effective model selection was performed to select the best sparsity threshold leading to the lowest cost function.
Model training
We trained models under several configurations. We trained a deep belief network (DBN) model without edges from miRNAs to mRNAs, the mCADET model with edges from miRNAs to mRNAs but without regularization, and the mCADET model with edges from miRNAs to mRNAs and with regularization (different sparsity ratios), and finally a the mCADET model with random permutation of miRNAs and mRNAs across tumors where the statistical relationships between miRNAs and mRNAs were fully disrupted. We compared the reconstruction errors for these models to choose the best model. The reconstruction errors are the difference between the raw input data and the reconstructed input predicted from the model. The model with the best reconstruction error is chosen to conduct the evaluations of biological representations.
Evaluations
We used the experimental results from miRTarBase [22] as “gold standard” to evaluate the prediction by our models and those by sequencebased and correlationbased methods. The accuracies of regulatory miRNAmRNA pairs predicted by mCADET models were compared with ones predicted by correlationbased and sequencebased analysis separately. For the correlation analysis, we run pairwise linear regression on our dataset to identify pairs with statistically significance, corrected by false discovery. For the sequencebased analysis, we used the predicted miRNAmRNA interaction acquired from the miRDB database [23]. Since it is difficult to assess the true negative rate, we mainly concentrate on evaluating models’ capabilities in identifying the true positives, i.e., the recall and positive predictive value (PPV) of each method.
To test whether the PPVs for two methods are significantly different from each other, we calculate the zscore as follows,
where p represents the PPV for mCADETbased analysis, correlationbased analysis and sequencebased analysis separately; n represents the number of predicted mRNAs. The z value between mCADET versus sequencebased model and the z value between mCADET versus correlationbased analysis were calculated separately. To quantitatively evaluate the difference between precision and recall for two groups, we calculated the F1 score that evaluates weighted average of precision and recall [33].
Finally, we also validated predicted mRNAmiRNA pairs by checking the agreement with literatures.
Results
Model training
We first assessed the capability of models to capture and represent statistical structures of data by comparing the reconstruction errors of different models in a series of crossvalidation experiments. We compared different DBN models with a fixed model architecture with four hidden layers and each layer has 1500, 1000, 500, and 250 nodes respectively. The baseline DBN is a DBN model without edges from miRNA to mRNA; the Model1_semi_spa_0.2 is a mCADET model that allows edges from miRNA to mRNA (i.e., the observed and first hidden layer forms a semiRBM) with a sparsity threshold 0.2; Model3_semi_spa_0.1 is an mCADET model with edges from miRNA to mRNA with a sparsity threshold 0.1. This means that we added a penalty to the activation of hidden units to allow 10% of the hidden units to be active. The result shows that the model with edges from miRNA to mRNA and with a sparsity threshold 0.2 has the lowest reconstruction error (Fig. 2 and Table 1). The result (Table 1) shows that the model allowing edges with a sparsity threshold 0.2 has the lowest reconstruction error. Therefore, the result analysis from the perspective of biological knowledge showed below is all based on this model.
The following interesting observations are noteworthy: 1) Adding causal edges between miRNA and mRNA improves the capability of a model to capture the overall statistical structures of data. Indicating that these potential causal edges enabled the model to capture the statistical relationships between miRNA and mRNAs that cannot be fully explained by coregulation. 2) Right amount of regularization enhances the capability of model to capture the statistical structures. This is reflected by the fact that model1_semispa_0.2 outperforms models without regularization (model2_semi_no_spa) and the model with too few parameters (model3_semi_spa_0.1 that only allow 10% of the hidden nodes to be active).
Statistical evaluation of predicted miRNAmRNA interaction
In the deep learning model, the weights of the direct edges from miRNAs to mRNAs reflect the strength of interaction between miRNA and mRNA. Therefore, we used the weights trained by the deep learning models to perform the miRNAmRNA interaction analysis. We only keep the top 5% absolute weights for each miRNA and get a corresponding mRNA list.
We compared the interactions predicted by the mCADET with the ones predicted by the correlationanalysis and the sequencebased analysis. As a concrete example, we showed the results of different methods predicting the targets of miR374a, as shown in Fig. 3. Apparently, sequencbased methods predicted the largest number of potential targets and very few of them match experimental results (PPV ~ 6%). In other words, majority of the predicted targets by sequencebased approach is false positive. Compared with the sequencebased method, the correlation analysis returns less targets. It only finds high correlations between the expression of mRNAs and its regulating miRNAs. In this case, it found 2350 mRNAs with relatively high correlation scores with miR374a, which means that those mRNAs have higher probability of interacting with miR374a.
Compared both sequencebased and correlationbased analyses, the number of targets predicted by the mCADET is the smallest. However, the mCADET achieves the highest PPV and best overall performance in comparison to the other two approaches. By comparing the overlap rates between the predicted targets and the experimentally validated targets, the mCADET performs the best as shown in Fig. 3 and Table. 2. In the light that computational predictions eventually need to be experimentally validated, a high PPV is a very desirable characteristics for prediction models. If one interprets the results from Table 2 literally, close to half (43%) of predicted target potentially can be verified by experiments, whereas only 6 and 19% of predictions by sequencebased and correlationbased can be verified. For example, mRNA MTPN and WWP1 are false predicted by sequencebased analysis, however, mCADET gave the interaction a small weight.
We used zscore to test the significant difference of PPVs for each two analyses. After the calculation, the z value and pvalue between modelbased analysis and correlationbased analysis is 2.67 and 0.045 respectively. The z value and pvalue between modelbased analysis and sequencebased analysis is 3.25 and 0.038 respectively. Both of z scores are bigger than 1.96 [34] and pvalues are less than 0.05, which shows that each of two groups is significantly different from each other. We could also conclude from Table 2 that the performance of our mCADET model is better than sequencedbased and correlationbased model. Besides, the sensitivity of the baseline DBN is 0.61 compared with the mCADET 0.64 and the PPV of the baseline DBN is 0.28 compared with the mCADET 0.43.
mCADET provides insights of different mechanisms for correlation between miRNAs and mRNAs
As shown in previous sections, adding causal edges in mCADET model can enhance the capability of the model to capture the overall statistical.
Structures including both miRNAs and mRNAs. Comparison to experimentally validated targets indicates that mCADET is more likely to capture the causal relationships between miRNAs and mRNAs. We further examined the examples that strong correlations between miRNAs and mRNAs are explained by different mechanisms in the results learned from the mCADET model.
Correlation analysis assigns miR125a and HYAL1 pair a strong correlation value 0.78 with a pvalue 0.03 and, in the mCADET model, the two RNAs are strongly connected to a common hidden node and the candidate causal edge between them does not pass our selection threshold. In other words, the model detected that the two RNAs were regulated by a hidden regulator (potentially a common transcription factor), and there was no strong causal relationship between the two RNAs as shown in Fig. 4a. On the other hand, miR374a and CCND1 pair also shows a strong correlation, and mCADET detected a strong direct edge from miR374a to CCND1 (as shown in Fig. 4b), indicating a direct regulatory mechanism which is supported by literature [35].
Interaction network of miRNAtargets
It is quite common that certain miRNAs share target mRNAs and form a miRNA regulatory network. To test whether the results of the mCADET can be used to search for such networks. We identified top 20 mRNAs associated with a miRNA and organized the miRNAs and mRNAs in a plot shown in Fig. 5. Interestingly, our methods correctly identified members of miRNA families (e.g., miR146 s and miR374 s) sharing target mRNAs in a pure datadriven fashion, without utilizing any knowledge of sequence similarity among the members of the miRNA family [36]. This provides additional evidence that our model can correctly detect the causal relationships from distinct miRNAs and to a common set of target mRNAs.
In addition to detect common targets of a miRNA family, our model can also detect the common functional impacts of distinct miRNAs. For example, our model can detect that CCND1 is the shared target of miR17 and miR20a [37]. More such relationships can be found in a broader analysis of our results, which are not shown in Fig. 5.
Literaturebased evaluation of predicted miRNAmRNA interactions
Previous research has accumulated a rich body of knowledge of regulatory relationships between miRNAs and important cancer drivers. We searched our results to identify predicted regulator miRNAs for certain common cancer driver genes. Many of them are reported in the literature. Table 3 lists examples of predicted mRNAmiRNA pairs validated by literatures.
Discussion
MicroRNAs play a significant role in regulating gene expression under physiological and pathological conditions. In particular, genomic alterations (amplification/deletion) of miRNAs in cancers have significant impacts on cancer development, disease progression, and therapy responses [38,39,40]. Thus, revealing the functional impacts of miRNAs in cancer will advance cancer biology. As shown in this report, previous methods of identifying targets of miRNAs have significant limitations. By combining deep learning and causal inference, the reported mCADET model achieved significantly identifying targets of miRNAs. Particularly, the improved PPV will convince cancer biologists to carry out validation experiments with much high confidence, thus helping to advance cancer biology.
The reported mCADET model is motivated by biological insights of the data related to miRNAs and mRNAs. It combines the strength of deep learning and causal inference in solving this important biological problem. The superior performance of the model reflects the importance of integrating biological insights with advance machine learning technology. Possible future improvement of the model includes (but not necessarily limited to) combining genomic alteration data to map hidden nodes to concrete biological entities as we did in mining yeast gene expression data [31].
Conclusions
In this study, we investigated the utility of the mCADET model to simultaneously infer the states of cellular signaling system regulating coexpression of miRNAs and mRNAs, while capturing their causal relationships in a datadriven fashion. This model can be used by miRNA researchers to systematically search for miRNAs that play significant roles in cancers and understand their disease mechanism which, we anticipate, will make significant advances in cancer biology, beyond what are reported here.
Abbreviations
 DBN:

Deep belief network
 mCADET:

miRNA causal deep net
 miRNAs:

MicroRNAs
 mRNAs:

Messenger RNAs
 PPV:

Positive predictive value
 RBM:

Restricted boltzmann machines
 TCGA:

The cancer genome atlas
 TF:

Transcription factor
References
 1.
Ardekani AM, Naeini MM. The role of MicroRNAs in human diseases. Avicenna journal of medical biotechnology. 2010;2(4):161–79.
 2.
Croce CM. Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet. 2009;10(10):704–14.
 3.
Peng Y, Croce CM. The role of MicroRNAs in human cancer. Signal transduction and targeted therapy. 2016;1:15004.
 4.
Iorio MV, Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. Embo Mol Med. 2012;4(3):143–59.
 5.
Iorio MV, Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review (vol 4, pg 143, 2012). Embo Mol Med. 2017;9(6):852–2.
 6.
Croce CM. Causes and consequences of microRNA dysregulation in cancer. EJC Suppl. 2010;8(5):8–8.
 7.
Lin SB, Gregory RI. MicroRNA biogenesis pathways in cancer. Nat Rev Cancer. 2015;15(6):321–33.
 8.
Chen CZ. MicroRNAs as oncogenes and tumor suppressors. New Engl J Med. 2005;353(17):1768–71.
 9.
Kurozumi S, Yamaguchi Y, Kurosumi M, Ohira M, Matsumoto H, Horiguchi J. Recent trends in microRNA research into breast cancer with particular focus on the associations between microRNAs and intrinsic subtypes. J Hum Genet. 2017;62(1):15–24.
 10.
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNAtarget interactions. Nucleic Acids Res. 2009;37(Database):D105–10.
 11.
Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNAceRNA, miRNAncRNA and proteinRNA interaction networks from largescale CLIPSeq data. Nucleic Acids Res. 2014;42(Database issue):D92–7.
 12.
Lewis BP, Shih IH, JonesRhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell. 2003;115(7):787–98.
 13.
Dweep H, Sticht C, Pandey P, Gretz N. miRWalkdatabase: prediction of possible miRNA binding sites by "walking" the genes of three genomes. J Biomed Inform. 2011;44(5):839–47.
 14.
Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z. MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci Rep. 2015;5:8004.
 15.
RiffoCampos AL, Riquelme I, BrebiMieville P. Tools for sequencebased miRNA target prediction: what to choose? Int J Mol Sci. 2016;17(12).
 16.
Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in drosophila. Genome Biol. 2003;5(1):R1.
 17.
Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. Rna. 2004;10(10):1507–17.
 18.
Kozomara A, GriffithsJones S. miRBase: integrating microRNA annotation and deepsequencing data. Nucleic Acids Res. 2011;39(Database):D152–7.
 19.
Pinzon N, Li B, Martinez L, Sergeeva A, Presumey J, Apparailly F, Seitz H. microRNA target prediction programs predict many false positives. Genome Res. 2017;27(2):234–45.
 20.
Li X, Quon G, Lipshitz HD, Morris Q. Predicting in vivo binding sites of RNAbinding proteins using mRNA secondary structure. Rnaa Publication of the Rna. Society. 2010;16(6):1096–107.
 21.
Giles CB, GirijaDevi R, Dozmorov MG, Wren JD. mirCoX: a database of miRNAmRNA expression correlations derived from RNAseq metaanalysis. Bmc Bioinformatics. 2013;14(Suppl 14):S17.
 22.
Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, Yang CD, Hong HC, Wei TY, Tu SJ, et al. miRTarBase 2016: updates to the experimentally validated miRNAtarget interactions database. Nucleic Acids Res. 2016;44(D1):D239–47.
 23.
Wang X. miRDB: a microRNA target prediction and functional annotation database with a wiki interface. Rna. 2008;14(6):1012–7.
 24.
Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2015;43(Database issue):D146–52.
 25.
Wang S, Li W, Lian B, Liu X, Zhang Y, Dai E, Yu X, Meng F, Jiang W, Li X. TMREC: a database of transcription factor and MiRNA regulatory cascades in human diseases. PLoS One. 2015;10(5):e0125222.
 26.
Han H, Shim H, Shin D, Shim JE, Ko Y, Shin J, Kim H, Cho A, Lee EKT, Kim H, et al. TRRUST: a reference database of human transcriptional regulatory interactions. Sci Rep. 2015;5.
 27.
Salakhutdinov R, Mnih A, Hinton GE. Restricted Boltzmann Machines for Collaborative Filtering. Proceedings of the 24th international conference on. Mach Learn. 2007:791–8.
 28.
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.
 29.
Chen L, Cai C, Chen V, Lu X. Transspecies learning of cellular signaling systems with bimodal deep belief networks. Bioinformatics. 2015.
 30.
Chen L, Cai C, Chen V, Lu X. Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC bioinformatics. 2016;17(Suppl 1):9.
 31.
Chen LJ, Cai CH, Chen V, Lu XH. Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. Bmc Bioinformatics. 2016;17.
 32.
H Lee CE, Ng AY. Sparse deep belief net model for visual area V2. In: NIPS: 2008; 2008.
 33.
Goutte C, Gaussier E. A probabilistic interpretation of precision, recall and Fscore, with implication for evaluation. Lect Notes Comput Sc. 2005;3408:345–59.
 34.
Shen LL, Toyota M, Kondo Y, Lin E, Zhang L, Guo Y, Hernandez NS, Chen XL, Ahmed S, Konishi K, et al. Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci U S A. 2007;104(47):18654–9.
 35.
Cloonan N, Brown MK, Steptoe AL, Wani S, Chan WL, Forrest AR, Kolle G, Gabrielli B, Grimmond SM. The miR175p microRNA is a key regulator of the G1/S phase cell cycle transition. Genome Biol. 2008;9(8).
 36.
Li YQ, Xu YD, Yu CD, Zuo WS. Associations of miR146a and miR146b expression and breast cancer in very young women. Cancer Biomark. 2015;15(6):881–7.
 37.
Mihailovich M, Bremang M, Spadotto V, Musiani D, Vitale E, Varano G, Zambelli F, Mancuso FM, Cairns DA, Pavesi G, et al. miR1792 finetunes MYC expression and function to ensure optimal B cell lymphoma growth. Nat Commun. 2015;6.
 38.
Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A. 2006;103(7):2257–61.
 39.
Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer. 2006;6(11):857–66.
 40.
Cheng CJ, Bahal R, Babar IA, Pincus Z, Barrera F, Liu C, Svoronos A, Braddock DT, Glazer PM, Engelman DM, et al. MicroRNA silencing for cancer therapy targeted to the tumour microenvironment. Nature. 2015;518(7537):107–10.
Acknowledgements
The authors would like to thank Mr. Sanghoon Lee for discussion and suggestions.
Funding
This work and publication have been partially supported by the U54HG008540 and R01LM012011.
Availability of the data and materials
The Matlab code is available upon request.
About this supplement
This article has been published as part of BMC Medical Genomics Volume 11 Supplement 6, 2018: Proceedings of the 29th International Conference on Genome Informatics (GIW 2018): medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume11supplement6.
Author information
Affiliations
Contributions
Conceived and designed the experiments: LC, XL; Performed the experiments: LC; Analyzed the data: LC; Wrote the paper: LC, XL. All authors read and approved the final manuscript.
Corresponding author
Correspondence to Lujia Chen.
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Chen, L., Lu, X. Discovering functional impacts of miRNAs in cancers using a causal deep learning model. BMC Med Genomics 11, 116 (2018) doi:10.1186/s1292001804320
Published
DOI
Keywords
 Deep learning
 Causal discovery
 miRNA and mRNA