Skip to main content
SpringerLink
Log in

DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The advancements in the field of high throughput analysis show abnormal expression of long non-coding RNAs (lncRNAs) in many complex diseases. Accurately identifying the disease association of lncRNA is essential in understanding their role in disease mechanism and subsequent therapy. The contemporary methods for predicting lncRNA-disease association use heterogeneous information learned from different biological sources such as lncRNAs, miRNAs, and diseases. However, learning topological features from diverse network structured data is one of the limiting factors of these methods. To address this challenge, we propose a method for lncRNA-disease association prediction based on Deep Belief Network (DBN), referred to as DBNLDA. In this method, three interaction networks such as lncRNA-miRNA similarity (LMS), disease-miRNA similarity (DMS), and lncRNA-disease association (LDA) network are constructed. A new framework based on the node embedding, DBN, and a neural network regression model is used to learn network and local representation of lncRNA-disease pairs. From the node embedding matrices of LMS, DMS, and LDA networks, lncRNA-disease features are learned by DBN layers. These DBN features are used to predict the association score by an ANN regression model. Compared to several state-of-the-art methods, DBNLDA obtained better AUC (0.96) and AUPR (0.967) under five-fold cross-validation. Case studies on breast, lung, and stomach cancer also affirmed the ability of DBNLDA in predicting potential lncRNAs associated with various diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bahari F, Emadi-Baygi M, Nikpour P et al (2015) miR-17-92 host gene, uderexpressed in gastric cancer and its expression was negatively correlated with the metastasis. Ind J Cancer 52(1):22

    Article  Google Scholar 

  2. Bhartiya D, Kapoor S, Jalali S, Sati S, Kaushik K, Sachidanandan C, Sivasubbu S, Scaria V (2012) Conceptual approaches for lncRNA drug discovery and future strategies. Expert Opin Drug Discov 7(6):503–513

    Article  Google Scholar 

  3. Chen G, Wang Z, Wang D, Qiu C, Liu M, Chen X, Zhang Q, Yan G, Cui Q (2012) LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res 41(D1):D983–D986

    Article  Google Scholar 

  4. Chen X (2015) KATZLDA: KATZ Measure for the lncRNA-disease association prediction. Sci Rep 5:16840

    Article  Google Scholar 

  5. Chen X, Yan CC, Luo C, Ji W, Zhang Y, Dai Q (2015) Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity. Sci Rep 5:11338

    Article  Google Scholar 

  6. Chen X, Yan CC, Luo C, Ji W, Zhang Y, Dai Q (2015) Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity. Sci Rep 5:11338

    Article  Google Scholar 

  7. Chen X, Yan CC, Zhang X, Li Z, Deng L, Zhang Y, Dai Q (2015) RBMMMDA: Predicting multiple types of disease-microRNA associations. Sci Rep 5:13877

    Article  Google Scholar 

  8. Chen X, You Z-H, Yan G-Y, Gong D-W (2016) IRWRLDA: Improved random walk with restart for lncRNA-disease association prediction. Oncotarget 7(36):57919

    Article  Google Scholar 

  9. Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on Machine learning, pp 233–240

  10. Ding L, Wang M, Sun D, Li A (2018) TPGLDA Novel Prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph. Sci Rep 8(1):1–11

    Google Scholar 

  11. Du Y, Hao X, Liu X (2018) Low expression of long noncoding rna cdkn2b-as1 in patients with idiopathic pulmonary fibrosis predicts lung cancer by regulating the p53-signaling pathway. Oncol Lett 15(4):4912–4918

    Google Scholar 

  12. Fan Y, Siklenka K, Arora SK, Ribeiro P, Kimmins S, Xia J (2016) miRNet-dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res 44 (W1):W135–W141

    Article  Google Scholar 

  13. Fu G, Wang J, Domeniconi C, Yu G (2018) Matrix factorization-based data fusion for the prediction of lncRNA–disease associations. Bioinformatics 34(9):1529–1537

    Article  Google Scholar 

  14. Goff LA, Rinn JL (2015) Linking RNA biology to lncRNAs. Genome Res 25(10):1456–1465

    Article  Google Scholar 

  15. Grover A, Leskovec J (2016) node2vec Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864

  16. Guo S, Zhang L, Zhang Y, Wu Z, He D, Li X, Wang Z (2019) Long non-coding RNA TUG1 enhances chemosensitivity in non-small cell lung cancer by impairing microRNA-221-dependent PTEN inhibition. Aging (Albany NY) 11(18):7553

    Article  Google Scholar 

  17. Hinton GE (2009) Deep belief networks. Scholarpedia 4(5):5947

    Article  Google Scholar 

  18. Huarte M (2015) The emerging role of lncRNAs in cancer. Nat Med 21(11):1253

    Article  Google Scholar 

  19. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907

  20. Lan W, Li M, Zhao K, Liu J, Wu F-X, Yi P, Wang J (2017) LDAP: A web server for lncRNA-disease association prediction. Bioinformatics 33(3):458–460

    Article  Google Scholar 

  21. Lee JT (2012) Epigenetic regulation by long noncoding RNAs. Science 338(6113):1435–1439

    Article  Google Scholar 

  22. Li JW, Gao C, Wang Y, Ma W, Tu J, Wang JP, Chen Z, Kong W, Cui Q (2014) A bioinformatics method for predicting long noncoding rnas associated with vascular disease. Sci China Life Sci 57(8):852–857

    Article  Google Scholar 

  23. Li J-H, Liu S, Zhou H, Qu L-H, Yang J-H (2014) starBase v2. 0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res D1:D92–D97

    Article  Google Scholar 

  24. Liang M, Li Z, Chen T, Zeng J (2014) Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinform 12(4):928–937

    Article  Google Scholar 

  25. L Liu X Y, Zhou J Q, Zhang G, Wang J, He Y, Chen C, Huang L (2018) Li, and SQ Li. LncRNA HULC promotes non-small cell lung cancer cell proliferation and inhibits the apoptosis by up-regulating sphingosine kinase 1 (SPHK1) and its downstream PI3k/akt pathway. Eur Rev Med Pharmacol Sci 22:8722–8730

    Google Scholar 

  26. Liu M-X, Chen X, Chen G, Cui Q-H, Yan G-Y (2014) A computational framework to infer human disease-associated long noncoding rnas. PloS One 9(1):e84408

    Article  Google Scholar 

  27. Lu C, Yang M, Luo F, Wu F-X, Li M, Yi P, Li Y, Wang J (2018) Prediction of lncRNA–disease associations based on inductive matrix completion. Bioinformatics 34(19):3357–3364

    Article  Google Scholar 

  28. Luo P, Li Y, Tian L-P, Wu F-X (2019) Enhancing the prediction of disease–gene associations with multimodal deep learning. Bioinformatics 35(19):3735–3742

    Article  Google Scholar 

  29. Luo Q, Chen Y (2016) Long noncoding RNAs and Alzheimer’s disease. Clin Intervent Aging 11:867

    Article  Google Scholar 

  30. Ning S, Zhang J, Wang P, Zhi H, Wang J, Liu Y, Gao Y, Guo M, Yue M, Wang L et al (2016) Lnc2cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers. Nucleic Acids Res 44(D1):D980–D985

    Article  Google Scholar 

  31. Paraskevopoulou MD, Hatzigeorgiou AG (2016) Analyzing miRNA–lncRNA interactions. In: long non-coding RNAs. Springer, pp 271–286

  32. Piao H-Y, Guo S, Wang Y, Zhang J (2019) Long noncoding RNA NALT1-induced gastric cancer invasion and metastasis via NOTCH signaling pathway. World J Gastroenterol 25(44):6508

    Article  Google Scholar 

  33. Ping P, Wang L, Kuang L, Ye S, Iqbal MFB, Pei T (2018) A novel method for lncRNA-disease association prediction based on an lncRNA-disease association network. IEEE/ACM Trans Comput Biol Bioinform 16(2):688–693

    Article  Google Scholar 

  34. Ren H, Yang X, Yang Y, Zhang X, Zhao R, Wei R, Zhang X, Yi Z (2018) Upregulation of lncRNA BCYRN1 promotes tumor progression and enhances epCAM expression in gastric carcinoma. Oncotarget 9(4):4851

    Article  Google Scholar 

  35. Song J, Wang Q, Zong L (2020) LncRNA MIR155HG contributes to smoke-related chronic obstructive pulmonary disease by targeting miR-128-5p/BRD4 axis. Biosci Rep 40(3)

  36. Sun J, Shi H, Wang Z, Zhang C, Liu L, Wang L, He W, Hao D, Liu S, Zhou M (2014) Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network. Mol BioSyst 10(8):2074–2081

    Article  Google Scholar 

  37. Wang G, Zheng X, Zheng Y, Cao R, Zhang M, Sun Y, Wu J (2019) Construction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveals functional genes in heart failure. Mol Med Rep 19(2):994–1003

    Google Scholar 

  38. Wang H, Shen Q, Zhang X, Yang C, Su C, Sun Y, Wang L, Fan X, Xu S (2017) The long non-coding RNA XIST controls non-small cell lung cancer proliferation and invasion by modulating miR-186-5p. Cell Physiol Biochem 41(6):2221–2229

    Article  Google Scholar 

  39. Wang P, Guo Q, Gao Y, Zhi H, Zhang Y, Liu Y, Zhang J, Yue M, Guo M, Ning S et al (2017) Improved method for prioritization of disease associated lncRNAs based on ceRNA theory and functional genomics data. Oncotarget 8(3):4642

    Article  Google Scholar 

  40. Wang S, Ke H, Zhang H, Ma Y, Ao L, Zou L, Yang Q, Zhu H, Nie J, Wu C et al (2018) LncRNA MIR100HG promotes cell proliferation in triple-negative breast cancer through triplex formation with p27 loci. Cell Death Disease 9(8):1–11

    Article  Google Scholar 

  41. Wang Y, Zeng J (2013) Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics 29(13):i126–i134

    Article  Google Scholar 

  42. Wu J, Chen H, Ye M, Wang B, Zhang Y, Sheng J, Meng T, Chen H (2019) Downregulation of long noncoding RNA HCP5 contributes to cisplatin resistance in human triple-negative breast cancer via regulation of PTEN expression. Biomed Pharmacother 115:108869

    Article  Google Scholar 

  43. Wu X, Lan W, Chen Q, Dong Y, Liu J, Peng W (2020) Inferring LncRNA-disease Associations Based: On Graph Autoencoder Matrix Completion. Computational Biology and Chemistry, pp 107282

  44. Xuan P, Cao Y, Zhang T, Kong R, Zhang Z (2019) Dual convolutional neural networks with attention mechanisms based method for predicting disease - related lncRNA genes. Front Gen 10:416

    Article  Google Scholar 

  45. Xuan P, Pan S, Zhang T, Liu Y, Sun H (2019) Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-disease Associations. Cells 8(9):1012

    Article  Google Scholar 

  46. Yao D, Zhan X, Zhan X, Kwoh CK, Li P, Wang J (2020) A random forest based computational model for predicting novel lncRNA-disease associations. BMC Bioinform 21(1):1–18

    Article  Google Scholar 

  47. Yu G, Fu G, Lu C, Ren Y, Wang J (2017) BRWLDA: Bi-random walks for predicting lncRNA-disease associations. Oncotarget 8(36):60429

    Article  Google Scholar 

  48. Han J, Kamber M, Pei J (2011) Data mining concepts and techniques. The Morgan Kaufmann Series in Data Management Systems, pp 5.4

Download references

Acknowledgements

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

Author information

Author notes

  1. Manu Madhavan

    Present address: Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala, India

    Manu Madhavan & G. Gopakumar

Authors
  1. Manu Madhavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Gopakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors contributed equally in conception, design and implementation of the proposed idea and manuscript preparation.

Corresponding author

Correspondence to Manu Madhavan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s10489-021-02675-x.

Availability of data and material

https://github.com/manumad/DBNLDA

Code availability

https://github.com/manumad/DBNLDA

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 136 KB)

(PDF 39.5 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Madhavan, M., Gopakumar, G. DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction. Appl Intell 52, 5342–5352 (2022). https://doi.org/10.1007/s10489-021-02675-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02675-x

Keywords

Search

Navigation