Skip to content
2000
image of GAALSMDA: A Graph Attention-based Fusion Network Integrating Dual Attention and BiLSTM for Microbe-Drug Association Prediction

Abstract

Introduction

Microbes have increasingly become critical new drug targets in human health. However, the paucity of known microbe-drug association data hinders drug discovery. Predicting potential microbe-drug associations can complement traditional experiments and accelerate drug development, making it crucial to develop efficient computational methods.

Methods

We proposed GAALSMDA, a graph attention-based fusion network. First, a microbe-drug heterogeneous network and feature matrix were constructed by integrating multiple similarities of microbes and drugs. Graph Attention Network (GAT) was used to mine low-dimensional features of microbes and drugs. Then, dual attention mechanism (CBAM) and Bidirectional Long Short-Term Memory (BiLSTM) were applied to fuse local and global features. Finally, a classifier output the likelihood scores of associations.

Results

The experimental results indicated that the AUC and AUPR evaluation indices of the model reached 0.9900±0.0011, 0.9958±0.0015 and 0.9492±0.0051, 0.9668±0.0042 in MDAD and aBiofilm datasets, respectively, and the prediction performance was significantly superior to that of existing prediction methods.

Discussion

The outstanding performance highlights GAALSMDA's ability to process sparse data and integrate multi-source information, addressing the limitations of previous models in terms of insufficient feature fusion. However, the similarity calculations of GIP and HIP may introduce parameter uncertainty, which still needs further optimization.

Conclusion

Our model demonstrates effectiveness and reliability in accurately inferring potential microbe-drug associations.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936395098250827094922
2025-09-18
2025-12-08

  • From This Site

    /content/journals/cbio/10.2174/0115748936395098250827094922
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Structure, function and diversity of the healthy human microbiome. Nature 2012 486 7402 207 214 10.1038/nature11234 22699609
    [Google Scholar]
  2. Sommer F. Bäckhed F. The gut microbiota — masters of host development and physiology. Nat. Rev. Microbiol. 2013 11 4 227 238 10.1038/nrmicro2974 23435359
    [Google Scholar]
  3. Ventura M. O’Flaherty S. Claesson M.J. Genome-scale analyses of health-promoting bacteria: Probiogenomics. Nat. Rev. Microbiol. 2009 7 1 61 71 10.1038/nrmicro2047 19029955
    [Google Scholar]
  4. Wen L. Ley R.E. Volchkov P.Y. Innate immunity and intestinal microbiota in the development of Type 1 diabetes. Nature 2008 455 7216 1109 1113 10.1038/nature07336 18806780
    [Google Scholar]
  5. de la Cuesta-Zuluaga J. Huus K.E. Youngblut N.D. Escobar J.S. Ley R.E. Obesity is the main driver of altered gut microbiome functions in the metabolically unhealthy. Gut Microbes 2023 15 2 2246634 10.1080/19490976.2023.2246634 37680093
    [Google Scholar]
  6. Schwabe R.F. Jobin C. The microbiome and cancer. Nat. Rev. Cancer 2013 13 11 800 812 10.1038/nrc3610 24132111
    [Google Scholar]
  7. Robilotti E. Deresinski S. Pinsky B.A. Norovirus. Clin. Microbiol. Rev. 2015 28 1 134 164 10.1128/CMR.00075‑14 25567225
    [Google Scholar]
  8. Xiang Y.T. Li W. Zhang Q. Timely research papers about COVID-19 in China. Lancet 2020 395 10225 684 685 10.1016/S0140‑6736(20)30375‑5 32078803
    [Google Scholar]
  9. Tutsoy O. Balikci K. Ozdil N.F. Unknown uncertainties in the COVID-19 pandemic: Multi-dimensional identification and mathematical modelling for the analysis and estimation of the casualties. Digit. Signal Process. 2021 114 103058 10.1016/j.dsp.2021.103058 33879984
    [Google Scholar]
  10. Zimmermann M. Patil K.R. Typas A. Maier L. Towards a mechanistic understanding of reciprocal drug–microbiome interactions. Mol. Syst. Biol. 2021 17 3 10116 10.15252/msb.202010116 33734582
    [Google Scholar]
  11. Klatt N.R. Cheu R. Birse K. Vaginal bacteria modify HIV tenofovir microbicide efficacy in African women. Science 2017 356 6341 938 945 10.1126/science.aai9383 28572388
    [Google Scholar]
  12. Yoon Y.K. Kim J. Moon C. Antimicrobial susceptibility of microorganisms isolated from patients with intraabdominal infection in korea: A multicenter study. J. Korean Med. Sci. 2019 34 47 309 10.3346/jkms.2019.34.e309 31808326
    [Google Scholar]
  13. Sun Y.Z. Zhang D.H. Cai S.B. Ming Z. Li J.Q. Chen X. MDAD: A special resource for microbe-drug associations. Front. Cell. Infect. Microbiol. 2018 8 424 10.3389/fcimb.2018.00424 30581775
    [Google Scholar]
  14. Rajput A. Thakur A. Sharma S. Kumar M. aBiofilm: A resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance. Nucleic Acids Res. 2018 46 D1 D894 D900 10.1093/nar/gkx1157 29156005
    [Google Scholar]
  15. Andersen P.I. Ianevski A. Lysvand H. Discovery and development of safe-in-man broad-spectrum antiviral agents. Int. J. Infect. Dis. 2020 93 268 276 10.1016/j.ijid.2020.02.018 32081774
    [Google Scholar]
  16. Zhu L. Duan G. Yan C. Wang J. Prediction of microbe-drug associations based on katz measure. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) San Diego, CA, USA 2019 183 187 10.1109/BIBM47256.2019.8983209
    [Google Scholar]
  17. Luo J Long Y. NTSHMDA: Prediction of human microbe-disease association based on random walk by integrating network topological similarity IEEE/ACM Trans Comput Biol Bioinform 2020; 2020 17 4 1341 51 10.1109/TCBB.2018.2883041
    [Google Scholar]
  18. Long Y. Wu M. Kwoh C.K. Luo J. Li X. Predicting human microbe–drug associations via graph convolutional network with conditional random field. Bioinformatics 2020 36 19 4918 4927 10.1093/bioinformatics/btaa598 32597948
    [Google Scholar]
  19. Yu Z. Huang F. Zhao X. Xiao W. Zhang W. Predicting drug–disease associations through layer attention graph convolutional network. Brief. Bioinform. 2021 22 4 bbaa243 10.1093/bib/bbaa243 33078832
    [Google Scholar]
  20. Tan Y. Zou J. Kuang L. GSAMDA: A computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder. BMC Bioinformatics 2022 23 1 492 10.1186/s12859‑022‑05053‑7 36401174
    [Google Scholar]
  21. Ma Q. Tan Y. Wang L. GACNNMDA: A computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier. BMC Bioinformatics 2023 24 1 35 10.1186/s12859‑023‑05158‑7 36732704
    [Google Scholar]
  22. Kuang H. Liu X. Tan H. Zhang Z. Zeng B. Wang L. GLNNMDA: A multimodal prediction model for microbe-drug associations based on global and local features. Sci. Rep. 2024 14 1 20847 10.1038/s41598‑024‑71837‑x 39242712
    [Google Scholar]
  23. Zhang P. Lin P. Li D. MGACL: Prediction drug–protein interaction based on meta-graph association-aware contrastive learning. Biomolecules 2024 14 10 1267 10.3390/biom14101267 39456200
    [Google Scholar]
  24. Wang C.C. Li T.H. Huang L. Chen X. Prediction of potential miRNA–disease associations based on stacked autoencoder. Brief. Bioinform. 2022 23 2 bbac021 10.1093/bib/bbac021 35176761
    [Google Scholar]
  25. Yang J. Lei X. Zhang F. Identification of circRNA ‐disease associations via multi‐model fusion and ensemble learning. J. Cell. Mol. Med. 2024 28 7 18180 10.1111/jcmm.18180 38506066
    [Google Scholar]
  26. Tutsoy O. Sumbul H.E. A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: Thyroid cancer diagnoses with randomly missing data. Brief. Bioinform. 2024 25 4 bbae344 10.1093/bib/bbae344 39007597
    [Google Scholar]
  27. Seo J. Jung H. Ko Y. PRID: Prediction model using rwr for interactions between drugs. Pharmaceutics 2023 15 10 2469 10.3390/pharmaceutics15102469 37896229
    [Google Scholar]
  28. Long Y. Zhang Y. Wu M. Heterogeneous graph attention networks for drug virus association prediction. Methods 2022 198 11 18 10.1016/j.ymeth.2021.08.003 34419588
    [Google Scholar]
  29. Wang S.H. Fernandes S.L. Zhu Z. Zhang Y.D. AVNC: Attention-based vgg-style network for COVID-19 diagnosis by CBAM. IEEE Sens. J. 2022 22 18 17431 17438 10.1109/JSEN.2021.3062442 36346097
    [Google Scholar]
  30. Nguyen-Vo T.H. Trinh Q.H. Nguyen L. Nguyen-Hoang P.U. Rahardja S. Nguyen B.P. iPromoter-Seqvec: Identifying promoters using bidirectional long short-term memory and sequence-embedded features. BMC Genomics 2022 23 S5 681 10.1186/s12864‑022‑08829‑6 36192696
    [Google Scholar]
  31. Wang L. Tan Y. Yang X. Kuang L. Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: From biological data to computational models. Brief. Bioinform. 2022 23 3 bbac080 10.1093/bib/bbac080 35325024
    [Google Scholar]
  32. Chen H. Chen K. Ensemble learning based on matrix completion improves microbe-disease association prediction. Brief. Bioinform. 2025 26 2 bbaf075 10.1093/bib/bbaf075 40037643
    [Google Scholar]
  33. Yan C. Duan G. Pan Y. Wu F.X. Wang J. DDIGIP: Predicting drug-drug interactions based on Gaussian interaction profile kernels. BMC Bioinformatics 2019 20 S15 538 10.1186/s12859‑019‑3093‑x 31874609
    [Google Scholar]
  34. Xu D. Xu H. Zhang Y. Wang M. Chen W. Gao R. MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities. J. Transl. Med. 2021 19 1 66 10.1186/s12967‑021‑02732‑6 33579301
    [Google Scholar]
  35. Niu M. Wang C. Chen Y. Zou Q. Luo X. Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction. BMC Biol. 2025 23 1 131 10.1186/s12915‑025‑02223‑w 40369616
    [Google Scholar]
  36. Hattori M Tanaka N Kanehisa M Goto S. SIMCOMP/SUBC OMP: Chemical structure search servers for network analyses Nucleic Acids Res 2010 38 Web Server issue W652 6 10.1093/nar/gkq367
    [Google Scholar]
  37. Kamneva O.K. Genome composition and phylogeny of microbes predict their co-occurrence in the environment. PLOS Comput. Biol. 2017 13 2 1005366 10.1371/journal.pcbi.1005366 28152007
    [Google Scholar]
  38. Liu J. Zhu L. Cao D. Identification of drought stress-responsive genes in rice by random walk with multi-restart probability on multiplex biological networks. Int. J. Mol. Sci. 2024 25 17 9216 10.3390/ijms25179216 39273165
    [Google Scholar]
  39. Ibrahim T.S. Saraya M.S. Saleh A.I. Rabie A.H. An efficient graph attention framework enhances bladder cancer prediction. Sci. Rep. 2025 15 1 11127 10.1038/s41598‑025‑93059‑5 40169776
    [Google Scholar]
  40. Aftab R. Qiang Y. Zhao J. Urrehman Z. Zhao Z. Graph neural network for representation learning of lung cancer. BMC Cancer 2023 23 1 1037 10.1186/s12885‑023‑11516‑8 37884929
    [Google Scholar]
  41. Mondal S. Maity R. Nag A. An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: Comprehensive analysis. Sci. Rep. 2025 15 1 4827 10.1038/s41598‑025‑85765‑x 39924575
    [Google Scholar]
  42. Shinde S.R. Bongale A.M. Dharrao D. Thepade S.D. An enhanced light weight face liveness detection method using deep convolutional neural network. MethodsX 2025 14 103229 10.1016/j.mex.2025.103229 40103768
    [Google Scholar]
  43. Kwak D. Choi J. Lee S. Rethinking breast cancer diagnosis through deep learning based image recognition. Sensors 2023 23 4 2307 10.3390/s23042307 36850906
    [Google Scholar]
  44. Liu Y. Liu J.B. Abnormal target detection method in hyperspectral remote sensing image based on convolution neural network. Comput. Intell. Neurosci. 2022 2022 1 8 10.1155/2022/9223552 35619769
    [Google Scholar]
  45. Fu J. Dual attention network for scene segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Long Beach, CA, USA 2019 3141 3149 10.1109/CVPR.2019.00326
    [Google Scholar]
  46. Hochreiter S. Schmidhuber J. Long short-term memory. Neural Comput. 1997 9 8 1735 1780 10.1162/neco.1997.9.8.1735 9377276
    [Google Scholar]
  47. Atlas L.G. Arockiam D. Muthusamy A. A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models. Sci. Rep. 2025 15 1 16642 10.1038/s41598‑025‑01104‑0 40360609
    [Google Scholar]
  48. Abdellah A.R. Abdelmoaty A. Ateya A.A. Abd El-Latif A.A. Muthanna A. Koucheryavy A. Accurate V2X traffic prediction with deep learning architectures. Front Artif Intell 2025 8 1565287 10.3389/frai.2025.1565287 40176965
    [Google Scholar]
  49. Xing J. Shi Y. Su X. Wu S. Discovering microbe-disease associations with weighted graph convolution networks and taxonomy common tree. Curr. Bioinform. 2024 19 7 663 673 10.2174/0115748936270441231116093650
    [Google Scholar]
  50. Fan L. Wang L. Zhu X. A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism. Sci. Rep. 2023 13 1 7396 10.1038/s41598‑023‑34438‑8 37149692
    [Google Scholar]
  51. Ali M.S. Kang H.S. Moon B.Y. Prevalence and characterization of ciprofloxacin-resistant Salmonella enterica spp. isolated from food animals during 2010–2023 in South Korea. Vet. Q. 2025 45 1 1 11 10.1080/01652176.2025.2473733 40091866
    [Google Scholar]
  52. Mahmoud A. Abo El-Ela F.I. Mahmoud R. Eco-friendly moxifloxacin removal using date seed waste and Ni-Fe LDH: Adsorption efficiency and antimicrobial potential. Ecotoxicol. Environ. Saf. 2025 297 118256 10.1016/j.ecoenv.2025.118256 40319707
    [Google Scholar]
  53. Razzaq Meo S. Van de Wiele T. Defoirdt T. Indole signaling in Escherichia coli: A target for antivirulence therapy? Gut Microbes 2025 17 1 2499573 10.1080/19490976.2025.2499573 40329925
    [Google Scholar]
  54. Campoli-Richards D.M. Monk J.P. Price A. Benfield P. Todd P.A. Ward A. Ciprofloxacin. Drugs 1988 35 4 373 447 10.2165/00003495‑198835040‑00003 3292209
    [Google Scholar]
  55. Gollapudi S. Kim C.H. Roshanravan B. Gupta S. Ciprofloxacin inhibits activation of latent human immunodeficiency virus type 1 in chronically infected promonocytic U1 cells. AIDS Res. Hum. Retroviruses 1998 14 6 499 504 10.1089/aid.1998.14.499 9566552
    [Google Scholar]
  56. Hoogkamp-Korstanje J A A. Moesker H. Bruyn G A W. Ciprofloxacin v placebo for treatment of Yersinia enterocoliticatriggered reactive arthritis. Ann. Rheum. Dis. 2000 59 11 914 917 10.1136/ard.59.11.914 11053072
    [Google Scholar]
  57. Barman Balfour J.A. Wiseman L.R. Moxifloxacin. Drugs 1999 57 3 363 373 10.2165/00003495‑199957030‑00007 10193688
    [Google Scholar]
  58. Greimel F. Scheuerer C. Gessner A. Efficacy of antibiotic treatment of implant-associated Staphylococcus aureus infections with moxifloxacin, flucloxacillin, rifampin, and combination therapy: An animal study. Drug Des. Devel. Ther. 2017 11 1729 1736 10.2147/DDDT.S138888 28652709
    [Google Scholar]
  59. Alharbi N.S. Khaled J.M. Kadaikunnan S. Prevalence of Escherichia coli strains resistance to antibiotics in wound infections and raw milk. Saudi J. Biol. Sci. 2019 26 7 1557 1562 10.1016/j.sjbs.2018.11.016 31762626
    [Google Scholar]
  60. Jayamani E. Mylonakis E. Effector triggered manipulation of host immune response elicited by different pathotypes of Escherichia coli. Virulence 2014 5 7 733 739 10.4161/viru.29948 25513774
    [Google Scholar]
  61. Poirel L. Madec J.Y. Lupo A. Antimicrobial resistance in Escherichia coli. Microbiol. Spectr. 2018 6 4 10.1128/microbiolspec.arba‑0026‑2017
    [Google Scholar]
  62. Xi X. Ruffieux H. A modeling framework for detecting and leveraging node-level information in Bayesian network inference. Biostatistics 2024 26 1 kxae021 10.1093/biostatistics/kxae021 38916966
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936395098250827094922
Loading
GAALSMDA: A Graph Attention-based Fusion Network Integrating Dual Attention and BiLSTM for Microbe-Drug Association Prediction
Bentham Science Publishers ; https://doi.org/10.2174/0115748936395098250827094922
/content/journals/cbio/10.2174/0115748936395098250827094922
/content/journals/cbio/10.2174/0115748936395098250827094922
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: Microbe-disease associations ; graph attention network ; drug discovery ; dual attention mechanism ; bidirectional long short-term memory ; feature fusion

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