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Volume 20, Issue 1
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

Complex and diverse microbial communities play a pivotal role in human health and have become a new drug target. Exploring the connections between drugs and microbes not only provides profound insights into their mechanisms but also drives progress in drug discovery and repurposing. The use of wet lab experiments to identify associations is time-consuming and laborious. Hence, the advancement of precise and efficient computational methods can effectively improve the efficiency of association identification between microorganisms and drugs.

Objective

In this experiment, we propose a new deep learning model, a new multiview comparative hypergraph attention network (MCHAN) method for human microbe–drug association prediction.

Methods

First, we fuse multiple similarity matrices to obtain a fused microbial and drug similarity network. By combining graph convolutional networks with attention mechanisms, we extract key information from multiple perspectives. Then, we construct two network topologies based on the above fused data. One topology incorporates the concept of hypernodes to capture implicit relationships between microbes and drugs using virtual nodes to construct a hyperheterogeneous graph. Next, we propose a cross-contrastive learning task that facilitates the simultaneous guidance of graph embeddings from both perspectives, without the need for any labels. This approach allows us to bring nodes with similar features and network topologies closer while pushing away other nodes. Finally, we employ attention mechanisms to merge the outputs of the GCN and predict the associations between drugs and microbes.

Results

To confirm the effectiveness of this method, we conduct experiments on three distinct datasets. The results demonstrate that the MCHAN model surpasses other methods in terms of performance. Furthermore, case studies provide additional evidence confirming the consistent predictive accuracy of the MCHAN model.

Conclusion

MCHAN is expected to become a valuable tool for predicting potential associations between microbiota and drugs in the future.

© 2025 Bentham Science Publishers
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2025-01-19

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References

  1. RelmanD.A. The human body as microbial observatory.Nat. Genet.200230213113310.1038/ng0202‑13111818955
     [Google Scholar]
  2. PengX. ChengL. YouY. TangC. RenB. LiY. XuX. ZhouX. Oral microbiota in human systematic diseases.Int. J. Oral Sci.20221411410.1038/s41368‑022‑00163‑735236828
     [Google Scholar]
  3. VenturaM. O’FlahertyS. ClaessonM.J. TurroniF. KlaenhammerT.R. van SinderenD. O’TooleP.W. Genome-scale analyses of health-promoting bacteria: Probiogenomics.Nat. Rev. Microbiol.200971617110.1038/nrmicro204719029955
     [Google Scholar]
  4. ByrdA.L. BelkaidY. SegreJ.A. The human skin microbiome.Nat. Rev. Microbiol.201816314315510.1038/nrmicro.2017.15729332945
     [Google Scholar]
  5. Al-NasiryS. AmbrosinoE. SchlaepferM. The interplay between reproductive tract microbiota and immunological system in human reproduction.Front Immunol202037810.3389/fimmu.2020.00378
     [Google Scholar]
  6. Marcos-ZambranoL.J. Karaduzovic-HadziabdicK. Loncar TurukaloT. PrzymusP. TrajkovikV. AasmetsO. BerlandM. GrucaA. HasicJ. HronK. KlammsteinerT. KolevM. LahtiL. LopesM.B. MorenoV. NaskinovaI. OrgE. PaciênciaI. PapoutsoglouG. ShigdelR. StresB. VilneB. YousefM. ZdravevskiE. TsamardinosI. Carrillo de Santa PauE. ClaessonM.J. Moreno-IndiasI. TruuJ. Applications of machine learning in human microbiome studies: A review on feature selection, biomarker identification, disease prediction and treatment.Front. Microbiol.20211263451110.3389/fmicb.2021.63451133737920
     [Google Scholar]
  7. DoréJ. SimrénM. ButtleL. GuarnerF. Hot topics in gut microbiota.United European Gastroenterol. J.201315311318https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4040776/10.1177/205064061350247724917977
     [Google Scholar]
  8. EppingaH. FuhlerG.M. PeppelenboschM.P. HechtG.A. Gut microbiota developments with emphasis on inflammatory bowel disease: report from the gut microbiota for health world summit 2016.Gastroenterology20161512e1e410.1053/j.gastro.2016.06.02427371370
     [Google Scholar]
  9. WuH. RaoQ. MaG.C. YuX.H. ZhangC.E. MaZ.J. Effect of triptolide on dextran sodium sulfate-induced ulcerative colitis and gut microbiota in mice.Front. Pharmacol.202010165210.3389/fphar.2019.0165232063856
     [Google Scholar]
  10. LiuB.N. LiuX.T. LiangZ.H. WangJ.H. Gut microbiota in obesity.World J. Gastroenterol.202127253837385010.3748/wjg.v27.i25.383734321848
     [Google Scholar]
  11. TaiN. WongF.S. WenL. The role of gut microbiota in the development of type 1, type 2 diabetes mellitus and obesity.Rev. Endocr. Metab. Disord.2015161556510.1007/s11154‑015‑9309‑025619480
     [Google Scholar]
  12. PeeneI. ElewautD. Changing the wolf from outside: How microbiota trigger systemic lupus erythematosus.Ann. Rheum. Dis.201978786786910.1136/annrheumdis‑2019‑21522131076388
     [Google Scholar]
  13. WuX. HeB. LiuJ. FengH. MaY. LiD. GuoB. LiangC. DangL. WangL. TianJ. ZhuH. XiaoL. LuC. LuA. ZhangG. Molecular insight into gut microbiota and rheumatoid arthritis.Int. J. Mol. Sci.201617343110.3390/ijms1703043127011180
     [Google Scholar]
  14. LubomskiM. DavisR.L. SueC.M. Gastrointestinal dysfunction in Parkinson’s disease.J. Neurol.202026751377138810.1007/s00415‑020‑09723‑531989280
     [Google Scholar]
  15. DuY. GaoX.R. PengL. GeJ.F. Crosstalk between the microbiota-gut-brain axis and depression.Heliyon202066e0409710.1016/j.heliyon.2020.e0409732529075
     [Google Scholar]
  16. SunY.Z. ZhangD.H. CaiS.B. MingZ. LiJ.Q. ChenX. MDAD: A special resource for microbe-drug associations.Front. Cell. Infect. Microbiol.2018842410.3389/fcimb.2018.0042430581775
     [Google Scholar]
  17. RajputA. ThakurA. SharmaS. KumarM. aBiofilm: A resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance.Nucleic Acids Res.201846D1D894D90010.1093/nar/gkx115729156005
     [Google Scholar]
  18. AndersenP.I. IanevskiA. LysvandH. VitkauskieneA. OksenychV. BjøråsM. TellingK. LutsarI. DumpisU. IrieY. TensonT. KanteleA. KainovD.E. Discovery and development of safe-in-man broad-spectrum antiviral agents.Int. J. Infect. Dis.20209326827610.1016/j.ijid.2020.02.01832081774
     [Google Scholar]
  19. ZhuL. DuanG. YanC. Prediction of microbe-drug associations based on Katz measure.IEEE international conference on bioinformatics and biomedicine2019183187
     [Google Scholar]
  20. YangH. DingY. TangJ. Inferring human microbe-drug associations via multiple kernel fusion on graph neural network.Knowl Based Syst202223810788810.1016/j.knosys.2021.107888
     [Google Scholar]
  21. LongY. WuM. KwohC.K. LuoJ. LiX. Predicting human microbe–drug associations via graph convolutional network with conditional random field.Bioinformatics202036194918492710.1093/bioinformatics/btaa59832597948
     [Google Scholar]
  22. LongY. MinW. LiuY. Ensembling graph attention networks for human microbe-drug association prediction.Bioinformatics2020362779786https://academic.oup.com/bioinformatics/article/36/Supplement_2/i779/10.1093/bioinformatics/btaa891
     [Google Scholar]
  23. LongY. LuoJ. Association mining to identify microbe drug interactions based on heterogeneous network embedding representation.IEEE J. Biomed. Health Inform.2021251266275https://ieeexplore.ieee.org/abstract/document/910484910.1109/JBHI.2020.299890632750918
     [Google Scholar]
  24. MaY. LiuQ. Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction.Comput. Biol. Med.202214510550310.1016/j.compbiomed.2022.10550335427986
     [Google Scholar]
  25. OhriK. KumarM. Review on self-supervised image recognition using deep neural networks.Knowl. Based Syst.2021224https://www.sciencedirect.com/science/article/abs/pii/S095070512100310.1016/j.knosys.2021.107090
     [Google Scholar]
  26. LuoX. JuW. QuM. Dualgraph: Improving semi-supervised graph classification via dual contrastive learning[c]//2022 ieee 38th international conference on data engineering.IEEE2022699712https://ieeexplore.ieee.org/abstract/document/9835533
     [Google Scholar]
  27. LuoX. JuW. GuY. MaoZ. LiuL. YuanY. ZhangM. Self-supervised graph-level representation learning with adversarial contrastive learning.ACM Trans. Knowl. Discov. Data202418212310.1145/3624018
     [Google Scholar]
  28. LuoX. JuW. QuM. Clear: Cluster-enhanced contrast for self-supervised graph representation learning[J].IEEE Transactions on Neural Networks and Learning Systems2022https://ieeexplore.ieee.org/document/9791433
     [Google Scholar]
  29. ChenW. WangH. LiangC. Deep multi-view contrastive learning for cancer subtype identification.Brief. Bioinform.2023245bbad28210.1093/bib/bbad28237539822
     [Google Scholar]
  30. ZhaoX. WuJ. ZhaoX. YinM. Multi-view contrastive heterogeneous graph attention network for lncRNA–disease association prediction.Brief. Bioinform.2023241bbac54810.1093/bib/bbac54836528809
     [Google Scholar]
  31. LiuW. TangT. LuX. MPCLCDA: Predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.Briefings in Bioinformatics2272023
     [Google Scholar]
  32. ChenY. HuY. HuX. FengC. ChenM. CoGO: A contrastive learning framework to predict disease similarity based on gene network and ontology structure.Bioinformatics202238184380438610.1093/bioinformatics/btac52035900147
     [Google Scholar]
  33. ZhengJ. QianY. HeJ. KangZ. DengL. Graph neural network with self-supervised learning for noncoding rna–drug resistance association prediction.J. Chem. Inf. Model.202262153676368410.1021/acs.jcim.2c0036735838124
     [Google Scholar]
  34. TangZ. LiZ. HouT. ZhangT. YangB. SuJ. SongQ. SiGra: single-cell spatial elucidation through an image-augmented graph transformer.Nat. Commun.20231415618https://www.nature.com/articles/s41467-023-41437-w10.1038/s41467‑023‑41437‑w37699885
     [Google Scholar]
  35. TangZ. LiuX. LiZ. ZhangT. YangB. SuJ. SongQ. SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment.Brief. Bioinform.2023246bbad338https://academic.oup.com/bib/article/24/6/bbad338/729199410.1093/bib/bbad33837798249
     [Google Scholar]
  36. LiG. BaiP. LiangC. Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.BMC Genomics202425173https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-024-
     [Google Scholar]
  37. YinN. FengF. LuoZ. Dynamic hypergraph convolutional network.IEEE 38th International Conference on Data Engineering202216211634https://ieeexplore.ieee.org/abstract/document/9835240
     [Google Scholar]
  38. ZhaoY. LuoX. JuW. Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting.CICDE202310.1109/ICDE55515.2023.00178
     [Google Scholar]
  39. WangL. DingY. TiwariP. A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites.Information Sciences20236304052https://www.sciencedirect.com/science/article/abs/pii/S0020025523001
     [Google Scholar]
  40. WangH. DingY. TangJ. Identify RNA-associated subcellular localizations based on multi-label learning using Chou’s 5-steps rule.BMC genomics2021221114https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-
     [Google Scholar]
  41. JiaX. JiangM. DongY. ZhuF. LinH. XinY. ChenH. Multimodal heterogeneous graph attention network.Neural Comput. Appl.202335433573372https://link.springer.com/article/10.1007/s00521-022-07862-6[J].10.1007/s00521‑022‑07862‑6
     [Google Scholar]
  42. DingJ. XuJ. WeiJ. A multi-scale multi-model deep neural network via ensemble strategy on high-throughput microscopy image for protein subcellular localization.Expert Systems with Applications2023212118744https://www.sciencedirect.com/science/article/abs/pii/S0957417422017
     [Google Scholar]
  43. WangY. SongJ. WeiM. DuanX. Predicting potential drug–disease associations based on hypergraph learning with subgraph matching.Interdiscip. Sci.2023152249261https://link.springer.com/article/10.1007/s12539-023-00556-010.1007/s12539‑023‑00556‑036906712
     [Google Scholar]
  44. ZhangL. ChenM. HuX. DengL. Graph convolutional network and contrastive learning small nucleolar rna (snorna) disease associations (gclsda): Predicting snorna–disease associations via graph convolutional network and contrastive learning.Int. J. Mol. Sci.2023241914429https://www.mdpi.com/1422-0067/24/19/1442910.3390/ijms24191442937833876
     [Google Scholar]
  45. AiC. YangH. DingY. Low rank matrix factorization algorithm based on multi-graph regularization for detecting drug-disease association.IEEE/ACM Transactions on Computational Biology and Bioinformatics2023https://ieeexplore.ieee.org/document/10122135
     [Google Scholar]
  46. KamnevaO.K. Genome composition and phylogeny of microbes predict their co-occurrence in the environment.PLoS Comput Biol20171321005366https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1
     [Google Scholar]
  47. HattoriM. TanakaN. KanehisaM. GotoS. SIMCOMP/SUBCOMP: Chemical structure search servers for network analyses.Nucleic Acids Res.201038Web ServerSuppl. 2W652W65610.1093/nar/gkq36720460463
     [Google Scholar]
  48. ShiX. ZhuJ. LongY. LiangC. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks.Brief. Bioinform.2023245bbad27810.1093/bib/bbad27837544658
     [Google Scholar]
  49. LiangC. ShangM. LuoJ. Cancer subtype identification by consensus guided graph autoencoders.Bioinformatics202137244779478610.1093/bioinformatics/btab53534289034
     [Google Scholar]
  50. NingQ. ZhaoY. GaoJ. ChenC. LiX. LiT. YinM. AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA–disease associations identification.Brief. Bioinform.2023242bbad094https://academic.oup.com/bib/article-abstract/24/2/bbad094/707612110.1093/bib/bbad09436907654
     [Google Scholar]
  51. ShangM. LiangC. LuoJ. Incomplete multi-view clustering by simultaneously learning robust representations and optimal graph structures.Information Sciences2023640119038https://www.sciencedirect.com/science/article/abs/pii/S0020025523006
     [Google Scholar]
  52. LiangC. WangL. LiuL. Multi-view unsupervised feature selection with tensor robust principal component analysis and consensus graph learning.Pattern recognition2023141109632https://www.sciencedirect.com/science/article/abs/pii/S0031320323003
     [Google Scholar]
  53. QianY. DingY. ZouQ. GuoF. Multi-view kernel sparse representation for identification of membrane protein types.IEEE/ACM Trans. Comput. Biol. Bioinformatics20232021234124510.1109/TCBB.2022.319132535857734
     [Google Scholar]
  54. ZouY. DingY. TangJ. GuoF. PengL. FKRR-MVSF: A fuzzy kernel ridge regression model for identifying DNA-binding proteins by multi-view sequence features via Chou’s five-step rule.Int. J. Mol. Sci.201920174175https://www.mdpi.com/1422-0067/20/17/417510.3390/ijms2017417531454964
     [Google Scholar]
  55. WangY. DingY. TangJ. DaiY. GuoF. CrystalM: A multi-view fusion approach for protein crystallization prediction.IEEE/ACM Trans. Comput. Biol. Bioinformatics202118132533531027046
     [Google Scholar]
  56. ChenT. KornblithS. NorouziM. A simple framework for contrastive learning of visual representations.International Conference on Machine Learning. PMLR202015971607
     [Google Scholar]
  57. YangC. LiuZ. ZhaoD. Network representation learning with rich text information.IJCAI (U. S.)2015201521112117https://dl.acm.org/doi/10.5555/2832415.2832542[C].
     [Google Scholar]
  58. SohnK. Improved deep metric learning with multi-class n-pair loss objective.Adv. Neural Inf. Process. Syst.201629
     [Google Scholar]
  59. DengL. HuangY. LiuX. LiuH. Graph2MDA: A multi-modal variational graph embedding model for predicting microbe–drug associations.Bioinformatics202238411181125https://academic.oup.com/bioinformatics/article/38/4/1118/643366910.1093/bioinformatics/btab79234864873
     [Google Scholar]
  60. YuZ. HuangF. ZhaoX. XiaoW. ZhangW. Predicting drug–disease associations through layer attention graph convolutional network.Brief. Bioinform.2021224bbaa24310.1093/bib/bbaa24333078832
     [Google Scholar]
  61. QuY. ZhangH. LiangC. DongX. KATZMDA: Prediction of miRNA-disease associations based on KATZ model.IEEE Access201863943395010.1109/ACCESS.2017.2754409
     [Google Scholar]
  62. LouZ. ChengZ. LiH. TengZ. LiuY. TianZ. Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood information.Brief. Bioinform.2022235bbac159https://academic.oup.com/bib/articleabstract/23/5/bbac159/658200510.1093/bib/bbac15935524503
     [Google Scholar]
  63. TianZ. YuY. FangH. XieW. GuoM. Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy.Brief. Bioinform.2023242bbac634https://academic.oup.com/bib/articleabstract/24/2/bbac634/700907710.1093/bib/bbac63436715986
     [Google Scholar]
  64. AkhtarR. YousafM. NaqviS.A.R. IrfanM. ZahoorA.F. HussainA.I. ChathaS.A.S. Synthesis of ciprofloxacin-based compounds: A review.Synth. Commun.201646231849187910.1080/00397911.2016.1234622
     [Google Scholar]
  65. SvenningsenS.W. FrederiksenR.F. CounilC. FickerM. LeisnerJ.J. ChristensenJ.B. Synthesis and antimicrobial properties of a ciprofloxacin and pamam-dendrimer conjugate.Molecules2020256138910.3390/molecules2506138932197523
     [Google Scholar]
  66. HaciogluM. HaciosmanogluE. Birteksoz-TanA.S. Bozkurt-GuzelC. SavageP.B. Effects of ceragenins and conventional antimicrobials on Candida albicans and Staphylococcus aureus mono and multispecies biofilms.Diagn. Microbiol. Infect. Dis.201995311486310.1016/j.diagmicrobio.2019.06.01431471074
     [Google Scholar]
  67. Marta Carreira Joana Pimenta Methicillin-Resistant Staphylococcus aureus and Candida albicans Secondary Bloodstream Co-Infection in a Patient with Tubular Oesophageal Duplication.Eur. J. Case Rep. Intern. Med.20207LATEST ONLINE00199110.12890/2020_00199133457357
     [Google Scholar]
  68. ZhangR. JonesM.M. MoussaH. KeskarM. HuoN. ZhangZ. VisserM.B. SabatiniC. SwihartM.T. ChengC. Polymer–antibiotic conjugates as antibacterial additives in dental resins.Biomater. Sci.201971287295https://pubs.rsc.org/en/content/articlehtml/2019/bm/c8bm01228h10.1039/C8BM01228H30468214
     [Google Scholar]
  69. SuciP. YoungM. Selective killing of aggregatibacter actinomycetemcomitans by ciprofloxacin during development of a dual species biofilm with streptococcus sanguinis.Arch. Oral Biol.201156101055106310.1016/j.archoralbio.2011.03.01321507381
     [Google Scholar]
  70. MiravitllesM. AnzuetoA. Moxifloxacin: A respiratory fluoroquinolone.Expert Opin. Pharmacother.20089101755177210.1517/14656566.9.10.175518570608
     [Google Scholar]
  71. SpecialeA. MusumeciR. BlandinoG. Minimal inhibitory concentrations and time-kill determination of moxifloxacin against aerobic and anaerobic isolates.International Journal of Antimicrobial Agents2002192111118https://www.sciencedirect.com/science/article/abs/pii/S0924857901004
     [Google Scholar]
  72. ThornsberryC. BrownN.P. DraghiD.C. EvangelistaA.T. YeeY.C. SahmD.F. Antimicrobial activity among multidrug-resistant Streptococcus pneumoniae isolated in the United States, 2001-2005.Postgrad. Med.2008120323810.3810/pgm.2008.09.suppl52.28218931469
     [Google Scholar]
  73. ChonJ.W. SeoK.H. BaeD. ParkJ.H. KhanS. SungK. Prevalence, toxin gene profile, antibiotic resistance, and molecular characterization of Clostridium perfringens from diarrheic and non-diarrheic dogs in Korea.J. Vet. Sci.201819336837410.4142/jvs.2018.19.3.36829486533
     [Google Scholar]
  74. GrillonA. SchrammF. KleinbergM. Comparative activity of ciprofloxacin, levofloxacin and moxifloxacin against Klebsiella pneumoniae, Pseudomonas aeruginosa and Stenotrophomonas maltophilia assessed by minimum inhibitory concentrations and time-kill studies.PloS one20161160156690https://journals.plos.org/plosone/article?id=10.1371/journal.pone.01566
     [Google Scholar]
  75. AlharbiN.S. KhaledJ.M. KadaikunnanS. AlobaidiA.S. SharafaddinA.H. AlyahyaS.A. AlmanaaT.N. AlsughayierM.A. ShehuM.R. Prevalence of escherichia coli strains resistance to antibiotics in wound infections and raw milk.Saudi J. Biol. Sci.201926715571562https://www.sciencedirect.com/science/article/pii/S1319562X1830294810.1016/j.sjbs.2018.11.01631762626
     [Google Scholar]
/content/journals/cbio/10.2174/0115748936288616240212073805
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MCHAN: Prediction of Human Microbe-drug Associations Based on Multiview Contrastive Hypergraph Attention Network
Curr Bioinform 20, 70 (2025); https://doi.org/10.2174/0115748936288616240212073805
/content/journals/cbio/10.2174/0115748936288616240212073805
/content/journals/cbio/10.2174/0115748936288616240212073805
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  • Article Type:     Research Article
Keyword(s): attention mechanisms; contrastive learning; deep learning; graph convolutional networks; hyperheterogeneous; Microbe–drug associations; multiview networks

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