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2000
Volume 20, Issue 10
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

Long non-coding RNAs (lncRNAs) are a category of more extended RNA strands that lack protein-coding abilities. Although they are not involved in the translation of proteins, studies have shown that they play essential regulatory functions in cells, regulating gene expression and cell biological processes. However, it is both costly and inefficient to determine the associations between lncRNAs and diseases through biological experiments. Therefore, there is an urgent need to develop convenient and fast computational methods to predict lncRNA-disease associations (LDAs) more efficiently.

Objective

Predicting disease-associated lncRNAs can help explore the mechanisms of action of lncRNAs in diseases, and this is crucial for early intervention and treatment of diseases.

Methods

In this paper, we propose an enhanced heterogeneous graph representation method for predicting LDAs, named GCGALDA. The GCGALDA first obtains the topological structure features of nodes by a biased random walk. Based on this, the neighboring nodes of a node are weighted using the attention mechanism to further mine the semantic association relationships between nodes in the graph data. Then, a graph convolution network (GCN) is used to transfer the neighborhood features of the node to the central node and combine them with the node's features so that the final node representation contains not only structural information but also semantic association information. Finally, the association score between lncRNA and disease is obtained by multilayer perceptron (MLP).

Results

As evidenced by the experimental findings, the GCGALDA outperforms other advanced models in terms of prediction accuracy on openly accessible databases. In addition, case studies on several human diseases further confirm the predictive ability of the GCGALDA.

Conclusion

In conclusion, the proposed GCGALDA model extracts multi-perspective features, such as topology, semantic association, and node attributes, obtains high-quality heterogeneous graph node representations, and effectively improves the performance of the LDA prediction model.

© 2025 Bentham Science Publishers
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2024-11-06
2025-12-14

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References

  1. ZhangY. YeF. GaoX. MCA-Net: Multi-feature coding and attention convolutional neural network for predicting lncRNA-disease association.IEEE/ACM Trans. Comput. Biol. Bioinformatics20221952907291910.1109/TCBB.2021.3098126 34283719
     [Google Scholar]
  2. WangY. ZhangL. KongR. Jun-mediated lncRNA-IMS promotes the meiosis of chicken spermatogonial stem cells via gga-miR-31-5p/stra8.Mol. Reprod. Dev.202390527528610.1002/mrd.23682 36966461
     [Google Scholar]
  3. ChenX. SunY.Z. GuanN.N. Computational models for lncRNA function prediction and functional similarity calculation.Brief. Funct. Genomics2019181588210.1093/bfgp/ely031 30247501
     [Google Scholar]
  4. LongY. WangX. YoumansD.T. CechT.R. How do lncRNAs regulate transcription?Sci. Adv.201739eaao211010.1126/sciadv.aao2110 28959731
     [Google Scholar]
  5. ZhangG. LanY. XieA. Comprehensive analysis of long noncoding RNA (lncRNA)-chromatin interactions reveals lncRNA functions dependent on binding diverse regulatory elements.J. Biol. Chem.201929443156131562210.1074/jbc.RA119.008732 31484726
     [Google Scholar]
  6. BhatA. GhatageT. BhanS. Role of transposable elements in genome stability: Implications for health and disease.Int. J. Mol. Sci.20222314780210.3390/ijms23147802 35887150
     [Google Scholar]
  7. LiJ. TianH. YangJ. GongZ. Long noncoding RNAs regulate cell growth, proliferation, and apoptosis.DNA Cell Biol.201635945947010.1089/dna.2015.3187 27213978
     [Google Scholar]
  8. FarooqiA.A. FayyazS. PoltronieriP. CalinG. MallardoM. Epigenetic deregulation in cancer: Enzyme players and non-coding RNAs.Semin. Cancer Biol.20228319720710.1016/j.semcancer.2020.07.013 32738290
     [Google Scholar]
  9. StatelloL. GuoC.J. ChenL.L. HuarteM. Gene regulation by long non-coding RNAs and its biological functions.Nat. Rev. Mol. Cell Biol.20212229611810.1038/s41580‑020‑00315‑9 33353982
     [Google Scholar]
  10. XuanP. ZhaoY. CuiH. Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction.IEEE/ACM Trans. Comput. Biol. Bioinformatics20232021480149110.1109/TCBB.2022.3209571 36173783
     [Google Scholar]
  11. ZhangY. YeF. XiongD. GaoX. LDNFSGB: Prediction of long non-coding RNA and disease association using network feature similarity and gradient boosting.BMC Bioinformatics202021137710.1186/s12859‑020‑03721‑0 32883200
     [Google Scholar]
  12. YuJ. XuanZ. FengX. ZouQ. WangL. A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier.BMC Bioinformatics201920139610.1186/s12859‑019‑2985‑0 31315558
     [Google Scholar]
  13. ZhuR. WangY. LiuJ.X. DaiL.Y. IPCARF: Improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.BMC Bioinformatics202122117510.1186/s12859‑021‑04104‑9 33794766
     [Google Scholar]
  14. LiJ. ZhaoH. XuanZ. A novel approach for potential human lncRNA-disease association prediction based on local random walk.IEEE/ACM Trans. Comput. Biol. Bioinformatics20211831049105910.1109/TCBB.2019.2934958 31425046
     [Google Scholar]
  15. ChenX. YanC.C. ZhangX. YouZ-H. Long non-coding RNAs and complex diseases: From experimental results to computational models.Brief. Bioinform.20162016bbw06010.1093/bib/bbw060 27345524
     [Google Scholar]
  16. FuG. WangJ. DomeniconiC. YuG. Matrix factorization-based data fusion for the prediction of lncRNA–disease associations.Bioinformatics20183491529153710.1093/bioinformatics/btx794 29228285
     [Google Scholar]
  17. WangH. TangJ. DingY. GuoF. Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment.Brief. Bioinform.2021225bbaa40910.1093/bib/bbaa409 33443536
     [Google Scholar]
  18. FanY. ChenM. PanX. GCRFLDA: Scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field.Brief. Bioinform.2022231bbab36110.1093/bib/bbab361 34486019
     [Google Scholar]
  19. XiW.Y. ZhouF. GaoY.L. LiuJ.X. ZhengC.H. LDCMFC: Predicting long non-coding rna and disease association using collaborative matrix factorization based on correntropy.IEEE/ACM Trans. Comput. Biol. Bioinformatics20232031774178210.1109/TCBB.2022.3215194 36251902
     [Google Scholar]
  20. ZhangJ. ZhangZ. ChenZ. DengL. Integrating multiple heterogeneous networks for novel LncRNA-disease association inference.IEEE/ACM Trans. Comput. Biol. Bioinformatics201916239640610.1109/TCBB.2017.2701379 28489543
     [Google Scholar]
  21. LiJ. WangD. YangZ. HEGANLDA: A computational model for predicting potential lncRNA-disease associations based on multiple heterogeneous networks.IEEE/ACM Trans. Comput. Biol. Bioinformatics2021202111
     [Google Scholar]
  22. ZhouJ.R. YouZ.H. ChengL. JiB.Y. Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks.Mol. Ther. Nucleic Acids20212327728510.1016/j.omtn.2020.10.040 33425486
     [Google Scholar]
  23. ZhangP. ZhangW. SunW. A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.Front. Genet.202314108448210.3389/fgene.2023.1084482 37274787
     [Google Scholar]
  24. FanY. ChenM. ZhuQ. WangW. Inferring Disease-associated microbes based on multi-data integration and network consistency projection.Front. Bioeng. Biotechnol.2020883110.3389/fbioe.2020.00831 32850711
     [Google Scholar]
  25. XieG.B. ChenR.B. LinZ.Y. Predicting lncRNA–disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.Brief. Bioinform.2023241bbac59510.1093/bib/bbac595 36592062
     [Google Scholar]
  26. ChenG. WangZ. WangD. LncRNADisease: A database for long-non-coding RNA-associated diseases.Nucleic Acids Res.201341D983D986 23175614
     [Google Scholar]
  27. WangJ.Z. DuZ. PayattakoolR. YuP.S. ChenC.F. A new method to measure the semantic similarity of GO terms.Bioinformatics200723101274128110.1093/bioinformatics/btm087 17344234
     [Google Scholar]
  28. LuoJ XiaoQ LiangC DingP. Predicting microRNA-disease associations using kronecker regularized least squares based on heterogeneous omics data.IEEE Access2017525031310.1109/ACCESS.2017.2672600
     [Google Scholar]
  29. van LaarhovenT. NabuursS.B. MarchioriE. Gaussian interaction profile kernels for predicting drug–target interaction.Bioinformatics201127213036304310.1093/bioinformatics/btr500 21893517
     [Google Scholar]
  30. GroverA. LeskovecJ. node2vec: Scalable Feature Learning for Networks.arXiv:1607006532016
     [Google Scholar]
  31. ClevertD-A. UnterthinerT. HochreiterS. Fast and accurate deep network learning by exponential linear units (ELUs).ar-Xiv:1511072892016
     [Google Scholar]
  32. KipfT.N. WellingM. Semi-Supervised Classification with Graph Convolutional Networks.arXiv:1609029072017
     [Google Scholar]
  33. LiuH. BingP. ZhangM. MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm.Comput. Struct. Biotechnol. J.2023211414142310.1016/j.csbj.2022.12.053 36824227
     [Google Scholar]
  34. LiM. FanY. ZhangY. LvZ. Using sequence similarity based on CKSNP features and a graph neural network model to identify miRNA–disease associations.Genes (Basel)20221310175910.3390/genes13101759 36292644
     [Google Scholar]
  35. LiP. TiwariP. XuJ. Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases.Knowl. Base. Syst.202225811004410.1016/j.knosys.2022.110044
     [Google Scholar]
  36. ZengM. LuC. FeiZ. DMFLDA: A deep learning framework for predicting lncRNA–disease associations.IEEE/ACM Trans. Comput. Biol. Bioinformatics20211862353236310.1109/TCBB.2020.2983958 32248123
     [Google Scholar]
  37. ShiZ. ZhangH. JinC. QuanX. YinY. A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.BMC Bioinformatics202122113610.1186/s12859‑021‑04073‑z 33745450
     [Google Scholar]
  38. XieG. HuangZ. LiuZ. LinZ. MaL. NCPHLDA: A novel method for human lncRNA–disease association prediction based on network consistency projection.Mol. Omics201915644245010.1039/C9MO00092E 31686064
     [Google Scholar]
  39. QinW. WangX. WangY. Functional polymorphisms of the lncRNA H19 promoter region contribute to the cancer risk and clinical outcomes in advanced colorectal cancer.Cancer Cell Int.201919121510.1186/s12935‑019‑0895‑x 31452627
     [Google Scholar]
  40. WuE.R. ChouY.E. LiuY.F. Association of lncRNA H19 Gene Polymorphisms with the Occurrence of Hepatocellular Carcinoma.Genes (Basel)201910750610.3390/genes10070506 31277475
     [Google Scholar]
  41. BrayF. FerlayJ. SoerjomataramI. SiegelR.L. TorreL.A. JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.201868639442410.3322/caac.21492 30207593
     [Google Scholar]
  42. HanP. LiJ. ZhangB. The lncRNA CRNDE promotes colorectal cancer cell proliferation and chemoresistance via miR-181a-5p-mediated regulation of Wnt/β-catenin signaling.Mol. Cancer2017161910.1186/s12943‑017‑0583‑1 28086904
     [Google Scholar]
  43. FangC. QiuS. SunF. Long non-coding RNA HNF1A-AS1 mediated repression of miR-34a/SIRT1/p53 feedback loop promotes the metastatic progression of colon cancer by functioning as a competing endogenous RNA.Cancer Lett.2017410506210.1016/j.canlet.2017.09.012 28943452
     [Google Scholar]
  44. DingK. LiaoY. GongD. ZhaoX. JiW. Effect of long non-coding RNA H19 on oxidative stress and chemotherapy resistance of CD133+ cancer stem cells via the MAPK/ERK signaling pathway in hepatocellular carcinoma.Biochem. Biophys. Res. Commun.2018502219420110.1016/j.bbrc.2018.05.143 29800569
     [Google Scholar]
  45. LeeY.J. LeeJ.M. LeeJ.S. Hepatocellular carcinoma: Diagnostic performance of multidetector CT and MR imaging-a systematic review and meta-analysis.Radiology201527519710910.1148/radiol.14140690 25559230
     [Google Scholar]
  46. XieG. MengT. LuoY. LiuZ. SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association.Mol. Ther. Nucleic Acids201918455510.1016/j.omtn.2019.07.022 31514111
     [Google Scholar]
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A Method of Enhancing Heterogeneous Graph Representation for Predicting the Associations between lncRNAs and Diseases
Curr Bioinform 20, 918 (2025); https://doi.org/10.2174/0115748936324054240903105015
/content/journals/cbio/10.2174/0115748936324054240903105015
/content/journals/cbio/10.2174/0115748936324054240903105015
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  • Article Type:     Research Article
Keyword(s): attention mechanism; biased random walk; graph convolution; Heterogeneous graph; semantic association; structural information; topological structure features

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