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BMC Med Genomics. 2018 Dec 31;11(Suppl 6):116. doi: 10.1186/s12920-018-0432-0.

Discovering functional impacts of miRNAs in cancers using a causal deep learning model.

Chen L1, Lu X2,3,4.

Author information

1
Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, USA. luc17@pitt.edu.
2
Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, USA.
3
Center for Causal Discovery, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, USA.
4
Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, USA.

Abstract

BACKGROUND:

Micro-RNAs (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, sequence-based 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 co-regulation and correlation as a result of causal regulation. The model utilizes a deep neural network to simulate transcription mechanism that leads to co-expression 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 pan-cancer miRNA and mRNA data from The Cancer Genome Atlas (TCGA) project to investigate mechanism of co-expression 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 high-order 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 sequence-based and correlation-based methods when comparing to experimental results.

CONCLUSION:

The mCADET model can simultaneously infer the states of cellular signaling system regulating co-expression of miRNAs and mRNAs, while capturing their causal relationships in a data-driven fashion.

KEYWORDS:

Causal discovery; Deep learning; miRNA and mRNA

PMID:
30598118
PMCID:
PMC6311958
DOI:
10.1186/s12920-018-0432-0
[Indexed for MEDLINE]
Free PMC Article

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