Skip to content
2000
Volume 20, Issue 6
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Aim

MicroRNAs (miRNAs), pivotal regulators in various biological processes, are closely linked to human diseases. This study aims to propose a computational model, SIDMF, for predicting miRNA-disease associations.

Background

Computational methods have proven efficient in predicting miRNA-disease associations, leveraging functional similarity and network-based inference. Machine learning techniques, including support vector machines, semi-supervised algorithms, and deep learning models, have gained prominence in this domain.

Objective

Develop a computational model that integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework to predict potential associations between miRNAs and diseases accurately.

Methods

SIDMF, introduced in this study, integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework. Through the reconstruction of the miRNA-disease association matrix, SIDMF predicts potential associations between miRNAs and diseases.

Results

The performance of SIDMF was evaluated using global Leave-One-Out Cross-Validation (LOOCV) and local LOOCV, achieving high Area Under the Curve (AUC) values of 0.9536 and 0.9404, respectively. Comparative analysis against other methods demonstrated the superior performance of SIDMF. Case studies on breast cancer, esophageal cancer, and prostate cancer further validated SIDMF's predictive accuracy, with a substantial percentage of the top 50 predicted miRNAs confirmed in relevant databases.

Conclusion

SIDMF emerges as a promising computational model for predicting potential associations between miRNAs and diseases. Its robust performance in global and local evaluations, along with successful case studies, underscores its potential contributions to disease prevention, diagnosis, and treatment.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936300759240712061707
2024-07-31
2025-09-11

  • From This Site

    /content/journals/cbio/10.2174/0115748936300759240712061707
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. LuM. ZhangQ. DengM. An analysis of human microRNA and disease associations.PLoS One2008310e342010.1371/journal.pone.0003420 18923704
     [Google Scholar]
  2. ChenH. ZhangZ. Similarity-based methods for potential human microRNA-disease association prediction.BMC Med. Genomics2013611210.1186/1755‑8794‑6‑12 23570623
     [Google Scholar]
  3. ShiH. XuJ. ZhangG. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes.BMC Syst. Biol.20137110110.1186/1752‑0509‑7‑101 24103777
     [Google Scholar]
  4. ChenX. LiuM.X. YanG.Y. RWRMDA: Predicting novel human microRNA–disease associations.Mol. Biosyst.20128102792279810.1039/c2mb25180a 22875290
     [Google Scholar]
  5. XuanP. HanK. GuoY. Prediction of potential disease-associated microRNAs based on random walk.Bioinformatics201531111805181510.1093/bioinformatics/btv039 25618864
     [Google Scholar]
  6. ChenX. NiuY.W. WangG.H. YanG.Y. Hamda: Hybrid approach for mirna-disease association prediction.J. Biomed. Inform.201776505810.1016/j.jbi.2017.10.014 29097278
     [Google Scholar]
  7. YouZ.H. HuangZ.A. ZhuZ. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.PLOS Comput. Biol.2017133e100545510.1371/journal.pcbi.1005455 28339468
     [Google Scholar]
  8. ZhangY. ChenM. ChengX. WeiH. Msfsp: A novel mirna–disease association prediction model by federating multiple-similarities fusion and space projection.Front. Genet.20201138910.3389/fgene.2020.00389 32425980
     [Google Scholar]
  9. LiX. A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization.Bioinformatics201733182829283610.1093/bioinformatics/btx339 28541468
     [Google Scholar]
  10. LiX. LinY. MengX. QiuY. HuB. An l 0 regularization method for imaging genetics and whole genome association analysis on alzheimer’s disease.IEEE J. Biomed. Health Inform.20212593677368410.1109/JBHI.2021.3093027 34181562
     [Google Scholar]
  11. QuQ. ChenX. NingB. Prediction of miRNA-disease associations by neural network-based deep matrix factorization.Methods20232121910.1016/j.ymeth.2023.02.003 36813017
     [Google Scholar]
  12. LiX. LinY. XieC. A clustering method unifying cell-type recognition and subtype identification for tumor heterogeneity analysis.IEEE/ACM Trans. Comput. Biol. Bioinformatics202320282283210.1109/TCBB.2022.3203185 36044493
     [Google Scholar]
  13. LiX. LiuL. ZhouJ. WangC. Heterogeneity analysis and diagnosis of complex diseases based on deep learning method.Sci. Rep.201881615510.1038/s41598‑018‑24588‑5 29670206
     [Google Scholar]
  14. LiX. MengX. ChenH. Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer.NPJ Syst. Biol. Appl.202391410.1038/s41540‑023‑00267‑8 36765073
     [Google Scholar]
  15. JiangQ. WangG. JinS. LiY. WangY. Predicting human microRNA-disease associations based on support vector machine.Int. J. Data Min. Bioinform.20138328229310.1504/IJDMB.2013.056078 24417022
     [Google Scholar]
  16. ChenX. YanG.Y. Semi-supervised learning for potential human microRNA-disease associations inference.Sci. Rep.201441550110.1038/srep05501 24975600
     [Google Scholar]
  17. ChenX. HuangL. Lrsslmda: Laplacian regularized sparse subspace learning for mirna-disease association prediction.PLOS Comput. Biol.20171312e100591210.1371/journal.pcbi.1005912 29253885
     [Google Scholar]
  18. PengJ. HuiW. LiQ. A learning-based framework for miRNA-disease association identification using neural networks.Bioinformatics201935214364437110.1093/bioinformatics/btz254 30977780
     [Google Scholar]
  19. ChenX. LiS.X. YinJ. WangC.C. Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization.Genomics2020112180981910.1016/j.ygeno.2019.05.021 31136792
     [Google Scholar]
  20. WangY.T. WuQ.W. GaoZ. NiJ.C. ZhengC.H. MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.BMC Med. Inform. Decis. Mak.202121S1Suppl. 113310.1186/s12911‑020‑01320‑w 33882934
     [Google Scholar]
  21. ZhouS. WangS. WuQ. AzimR. LiW. Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression.Comput. Biol. Chem.20208510720010.1016/j.compbiolchem.2020.107200 32058946
     [Google Scholar]
  22. LiuW. LinH. HuangL. Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder.Brief. Bioinform.2022233bbac10410.1093/bib/bbac104 35325038
     [Google Scholar]
  23. LiY. QiuC. TuJ. HMDD v2.0: A database for experimentally supported human microRNA and disease associations.Nucleic Acids Res.201442D1D1070D107410.1093/nar/gkt1023 24194601
     [Google Scholar]
  24. HarbeckN. Penault-LlorcaF. CortesJ. Breast cancer.Nat. Rev. Dis. Primers20195166
     [Google Scholar]
  25. IacovielloL. BonaccioM. de GaetanoG. DonatiM.B. Epidemiology of breast cancer, a paradigm of the “common soil” hypothesis.Semin. Cancer Biol.20217241010.1016/j.semcancer.2020.02.010
     [Google Scholar]
  26. MeiJ. YanT. HuangY. A DAAM1 3′-UTR SNP mutation regulates breast cancer metastasis through affecting miR-208a-5p-DAAM1-RhoA axis.Cancer Cell Int.20191915510.1186/s12935‑019‑0747‑8 30911286
     [Google Scholar]
  27. BahreiniF. RayzanE. RezaeiN. microRNA‐related single‐nucleotide polymorphisms and breast cancer.J. Cell. Physiol.202123631593160510.1002/jcp.29966 32716070
     [Google Scholar]
  28. KeklikoglouI. KoernerC. SchmidtC. MicroRNA-520/373 family functions as a tumor suppressor in estrogen receptor negative breast cancer by targeting NF-κB and TGF-β signaling pathways.Oncogene201231374150416310.1038/onc.2011.571 22158050
     [Google Scholar]
  29. AoifeJ. Microrna signatures predict oestrogen receptor, progesterone receptor and her2/neureceptor status in breast cancer.Breast Cancer Res.2009113118
     [Google Scholar]
  30. 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]
  31. Rios VelazquezE. ParmarC. LiuY. Somatic mutations drive distinct imaging phenotypes in lung cancer.Cancer Res.201777143922393010.1158/0008‑5472.CAN‑17‑0122 28566328
     [Google Scholar]
  32. LiJ. XuJ. ZhengY. Esophageal cancer: Epidemiology, risk factors and screening.Chin. J. Cancer Res.202133553554710.21147/j.issn.1000‑9604.2021.05.01 34815628
     [Google Scholar]
  33. MarabottoE. PellegattaG. SheijaniA.D. Prevention strategies for esophageal cancer—an expert review.Cancers2021139218310.3390/cancers13092183 34062788
     [Google Scholar]
  34. YunhuiB. ShuangfuY. KaiX. HongmeiG. Expression and clinical significance of mir-501-5p and lpar1 mrna in esophageal cancer.J Chin Phys202020201216061610
     [Google Scholar]
  35. MaoY. LiL. LiuJ. WangL. ZhouY. MiR-495 inhibits esophageal squamous cell carcinoma progression by targeting Akt1.Oncotarget2016732512235123610.18632/oncotarget.9981 27323412
     [Google Scholar]
  36. KatherineS. Microrna expression differentiates squamous epithelium from barrett’s esophagus and esophageal cancer.Dig. Dis. Sci.20135831783188
     [Google Scholar]
  37. FerlayJ. ErvikM. LamF. Global cancer observatory: International agency for research on cancer.2013Available From: https://gco.iarc.fr/en
    [Google Scholar]
  38. FerlayJ. ColombetM. SoerjomataramI. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods.Int. J. Cancer201914481941195310.1002/ijc.31937 30350310
     [Google Scholar]
  39. RawlaP. Epidemiology of prostate cancer.World J. Oncol.2019102638910.14740/wjon1191 31068988
     [Google Scholar]
  40. PanigrahiG.K. PraharajP.P. KittakaH. Exosome proteomic analyses identify inflammatory phenotype and novel biomarkers in African American prostate cancer patients.Cancer Med.2019831110112310.1002/cam4.1885 30623593
     [Google Scholar]
  41. GiovannucciE. RimmE.B. ColditzG.A. A prospective study of dietary fat and risk of prostate cancer.J. Natl. Cancer Inst.199385191571157910.1093/jnci/85.19.1571 8105097
     [Google Scholar]
  42. KolonelL.N. NomuraA.M.Y. CooneyR.V. Dietary fat and prostate cancer: Current status.J. Natl. Cancer Inst.199991541442810.1093/jnci/91.5.414 10070940
     [Google Scholar]
  43. LovegroveC. AhmedK. ChallacombeB. KhanM.S. PopertR. DasguptaP. Systematic review of prostate cancer risk and association with consumption of fish and fish-oils: Analysis of 495,321 participants.Int. J. Clin. Pract.20156918710510.1111/ijcp.12514 25495842
     [Google Scholar]
  44. PlatzE.A. LeitzmannM.F. MichaudD.S. WillettW.C. GiovannucciE. Interrelation of energy intake, body size, and physical activity with prostate cancer in a large prospective cohort study.Cancer Res.2003632385428548 14679023
     [Google Scholar]
  45. WillisM.S. WiansF.H.Jr The role of nutrition in preventing prostate cancer: A review of the proposed mechanism of action of various dietary substances.Clin. Chim. Acta20033301-2578310.1016/S0009‑8981(03)00048‑2 12636926
     [Google Scholar]
  46. WilsonK.M. GiovannucciE.L. MucciL.A. Lifestyle and dietary factors in the prevention of lethal prostate cancer.Asian J. Androl.201214336537410.1038/aja.2011.142 22504869
     [Google Scholar]
  47. WolkA. Diet, lifestyle and risk of prostate cancer.Acta Oncol.200544327728110.1080/02841860510029572 16076700
     [Google Scholar]
  48. KatiaR.M. The role of microRNAs 371 and 34a in androgen receptor control influencing prostate cancer behavior.Urolog Oncol.2015336267.e15267.e22
     [Google Scholar]
  49. DanielR. WuQ. WilliamsV. ClarkG. GuruliG. ZehnerZ. A panel of micrornas as diagnostic biomarkers for the identification of prostate cancer.Int. J. Mol. Sci.2017186128110.3390/ijms18061281 28621736
     [Google Scholar]
  50. MengD. YangS. WanX. A transcriptional target of androgen receptor, miR-421 regulates proliferation and metabolism of prostate cancer cells.Int. J. Biochem. Cell Biol.201673304010.1016/j.biocel.2016.01.018 26827675
     [Google Scholar]
/content/journals/cbio/10.2174/0115748936300759240712061707
Loading
Prediction of miRNA-disease Associations by Deep Matrix Decomposition Method based on Fused Similarity Information
Curr Bioinform 20, 545 (2025); https://doi.org/10.2174/0115748936300759240712061707
/content/journals/cbio/10.2174/0115748936300759240712061707
/content/journals/cbio/10.2174/0115748936300759240712061707
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): deep matrix factorization; disease semantic similarity; fused similarity information; miRNA; miRNA-disease associations; SIDMF

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test