Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

CGDGMDA-Net: discovering microbe-disease and drug associations through CTGAN and graph-based deep learning

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

This research proposes a novel method for enhancing the prediction of microbe-disease associations and the identification of possible drug treatments through the utilization of advanced computational strategies. The foundation of this approach is the integration of data from the Human Microbe-Disease Association Database with advanced techniques such as Conditional Tabular Generative Adversarial Networks for augmenting data and a combination of Graph Neural Networks and Graph Attention Networks for modeling the intricate relationships between microbes and diseases. Additionally, this study leverages the Bidirectional Encoder Representations from Transformers for the encoding of diseases and drugs, thereby improving the prediction capabilities for disease-drug associations. A thorough experimental analysis validates the efficacy of these approaches in decoding complex biological datasets, offering valuable insights into disease etiology and potential treatment pathways. The results highlight the significance of merging generative models, graph-based deep learning, and transformer-based natural language processing models to push forward the boundaries of biomedical research and its applications.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Algorithm 1
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The dataset used in this work is available in open domain and no new dataset is generated. The required data will be provided on request.

References

  • Angermueller C, Pärnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Molecular Systems Biology 12(7), 878 https://doi.org/10.15252/msb.20156651https://www.embopress.org/doi/pdf/10.15252/msb.20156651

  • Canziani A, Paszke A, Culurciello E (2017) An Analysis of Deep Neural Network Models for Practical Applications

  • Chen Y, Lei X (2022) Metapath aggregated graph neural network and tripartite heterogeneous networks for microbe-disease prediction. Front Microbiol 13:919380. https://doi.org/10.3389/fmicb.2022.919380

    Article  Google Scholar 

  • David L, Maurice C, Carmody R, Gootenberg D, Button J, Wolfe B, Ling A, Devlin A, Varma Y, Fischbach M, Biddinger S, Dutton R, Turnbaugh P (2013) Diet rapidly and reproducibly alters the gut microbiome. Nature 505https://doi.org/10.1038/nature12820

  • Del Chierico F, Rapini N, Deodati A, Matteoli MC, Cianfarani S, Putignani L (2022) Pathophysiology of type 1 diabetes and gut microbiota role. International Journal of Molecular Sciences 23(23) https://doi.org/10.3390/ijms232314650

  • Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding arXiv:1810.04805 [cs.CL]

  • Disbiome Research Team: Disbiome Database. (2023) https://disbiome.ugent.be/home. Accessed: insert-date-here

  • Donia M, Cimermancic P, Schulze C, Brown L, Martin J, Mitreva M, Clardy J, Linington R, Fischbach M (2014) A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell 158:1402–14. https://doi.org/10.1016/j.cell.2014.08.032

    Article  Google Scholar 

  • Du G, Zhang J, Jiang M, Long J, Lin Y, Li S, Tan KC (2021) Graph-based class-imbalance learning with label enhancement. IEEE Trans Neural Netw Learn Syst 34(9):6081–6095

    Article  MathSciNet  Google Scholar 

  • Fujihashi T, Koike-Akino T (2023) Graph-based eeg signal compression for human-machine interaction. IEEE Access

  • Guarner F, Malagelada J-R (2003) Gut flora in health and disease. Lancet 361(9356):512–519. https://doi.org/10.1016/S0140-6736(03)12489-0

    Article  Google Scholar 

  • Hooks KB, O’Malley MA (2017) Dysbiosis and its discontents. mBio 8(5), 10–11280149217 https://doi.org/10.1128/mbio.01492-17 https://journals.asm.org/doi/pdf/10.1128/mbio.01492-17

  • Hoque MN, Akter S, Mishu ID, Islam MR, Rahman MS, Akhter M, Islam I, Hasan MM, Rahaman MM, Sultana M, Islam T, Hossain MA (2021) Microbial co-infections in covid-19: Associated microbiota and underlying mechanisms of pathogenesis. Microbial Pathogenesis 156:104941. https://doi.org/10.1016/j.micpath.2021.104941

    Article  Google Scholar 

  • Hua M, Yu S, Liu T, Yang X, Wang H (2022) Mvgcnmda: Multi-view graph augmentation convolutional network for uncovering disease-related microbes. Interdisciplinary Sciences: Computational Life Sciences 14https://doi.org/10.1007/s12539-022-00514-2

  • Huang H-c, Chen Z-h, Li B-w, Ma Q-h, He H-d (2024) Festgcn: A frequency-enhanced spatio-temporal graph convolutional network for traffic flow prediction under adaptive signal timing. Appl Intell 1–17

  • Huang Y-A, You Z-H, Yan G, Wang X-S (2017) A novel approach based on katz measure to predict associations of human microbiota with non-infectious diseases. Bioinformatics 34https://doi.org/10.1093/bioinformatics/btx773

  • Hwang S, Kim C, Yang S, Kim E, Hart T, Marcotte E, Lee I (2018) Humannet v2: human gene networks for disease research. Nucl Acids Res 47https://doi.org/10.1093/nar/gky1126

  • Islam MA, Ahmed CF, Alam MT, Leung CK-S (2024) Graph-based substructure pattern mining with edge-weight. Applied Intelligence 54(5):3756–3785

    Article  Google Scholar 

  • Kang M, Martin A (2017) Microbiome and colorectal cancer: Unraveling host-microbiota interactions in colitis-associated colorectal cancer development. Seminars in Immunology 32https://doi.org/10.1016/j.smim.2017.04.003

  • Kang Y, Jia N, Cui R, Deng J (2021) A graph-based semi-supervised reject inference framework considering imbalanced data distribution for consumer credit scoring. Appl Soft Comput 105:107259

    Article  Google Scholar 

  • Knights D, Silverberg M, Weersma R, Gevers D, Dijkstra G, Huang H, Tyler A, Sommeren S, Imhann F, Stempak J, Huang H, Vangay P, Al-Ghalith G, Russell C, Sauk J, Knight J, Daly M, Huttenhower C, Xavier R (2014) Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med 6:107. https://doi.org/10.1186/s13073-014-0107-1

    Article  Google Scholar 

  • Koroteev MV (2021) BERT: A Review of Applications in Natural Language Processing and Understanding

  • Laarhoven T, Nabuurs S, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics (Oxford, England) 27:3036–43. https://doi.org/10.1093/bioinformatics/btr500

    Article  Google Scholar 

  • Lambrecht BN, Hammad H (2014) The immunology of asthma. Nat Immunol 16:45–56

    Article  Google Scholar 

  • Li C, Zhu X, Yan Y, Zhao Z, Su L, Zeng Q (2024) Mhgnn: Multi-view fusion based heterogeneous graph neural network. Appl Intell 1–19

  • Li H, Tang M, Mu Y, Wang Y, Yang T, Wang H (2024) Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of lanzhou city. Appl Intell, 1–23

  • Liu D, Liu J, Luo Y, He Q, Deng L (2022) Mgatmda: Predicting microbe-disease associations via multi-component graph attention network. IEEE/ACM Trans Comput Biol Bioinform 19(6):3578–3585. https://doi.org/10.1109/TCBB.2021.3116318

    Article  Google Scholar 

  • Liu Z, Wang Y, Luo Y, Luo C (2024) Graph-based few-shot incremental learning algorithm for unknown class detection. Appl Soft Comput 154:111363

    Article  Google Scholar 

  • Long Y, Luo J, Zhang Y, Xia Y (2020) Predicting human microbe-disease associations via graph attention networks with inductive matrix completion. Briefings in Bioinformatics 22(3):146. https://doi.org/10.1093/bib/bbaa146 https://academic.oup.com/bib/article-pdf/22/3/bbaa146/37963818/bbaa146.pdf

  • Long Y, Wu M, Liu Y, Kwoh CK, Luo J, Li X (2020) Ensembling graph attention networks for human microbe-drug association prediction. Bioinformatics 36(Suppl_2), 779–786 https://doi.org/10.1093/bioinformatics/btaa891

  • Lv X, Liu Z, Zhao Y, Xu G, You X (2023) Hbert: A long text processing method based on bert and hierarchical attention mechanisms. Int J Semantic Web Inform Syst (IJSWIS) 19(1):1–14

    Article  Google Scholar 

  • Ma L, Rabbany R, Romero-Soriano A (2021) Graph attention networks with positional embeddings. In: Pacific-Asia conference on knowledge discovery and data mining, pp. 514–527. Springer

  • Ma W, Zhang L, Zeng P, Huang C, Li J, Geng B, Yang J, Kong W, Zhou X, Cui Q (2016) An analysis of human microbe-disease associations. Briefings Bioinform 18(1):85–97. https://doi.org/10.1093/bib/bbw005 (https://academic.oup.com/bib/article-pdf/18/1/85/25408549/bbw005.pdf)

    Article  Google Scholar 

  • Mathew B (2014) A review on recent diseases caused by microbes, J Appl Environ Microbiol 2014, 2(4): 106-115

  • Methé B, Nelson KE, Pop M, Huot Creasy H, Giglio M, Huttenhower C, Gevers D, JF P, Abubucker S, Badger J, Chinwalla A, AM E, Fitzgerald M, Fulton R, Pepin K, EA L, Madupu R, Magrini V, Martin J, White O, (2012) A framework for human microbiome research. Nature 486:215–221. https://doi.org/10.1038/nature11209

  • Muegge B, Kuczynski J, Knights D, Clemente J, González A, Fontana L, Henrissat B, Knight R, Gordon J (2011) Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science (New York, N.Y.) 332, 970–4 https://doi.org/10.1126/science.1198719

  • Naik A, Patwardhan I, Joshi A (2023) Synthesizing microbiome-disease association data using gans. In: 2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC), pp. 1–6. https://doi.org/10.1109/ICACIC59454.2023.10435071

  • Qu J, Zhao Y, Yin J (2019) Identification and analysis of human microbe-disease associations by matrix decomposition and label propagation. Front Microbiol 10 https://doi.org/10.3389/fmicb.2019.00291

  • Sarkar S, Babar MF, Hassan MM, Hasan M, Santu SKK (2023) Exploring challenges of deploying bert-based nlp models in resource-constrained embedded devices. arXiv preprint arXiv:2304.11520

  • Read J, Perez-Cruz F (2014) Deep Learning for Multi-label classification

  • Scarselli F, Gori M, Tsoi A, Hagenbuchner M, Monfardini G (2009) Computational capabilities of graph neural networks. IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council 20:81–102. https://doi.org/10.1109/TNN.2008.2005141

    Article  Google Scholar 

  • Tan Y, Zou J, Kuang L, Wang X, Zeng B, Zhang Z, Wang L (2022) Gsamda: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder. BMC Bioinform 23(1):492. https://doi.org/10.1186/s12859-022-05053-7

    Article  Google Scholar 

  • Teh J, Berendsen E, Hoedt E, Kang S, Zhang J, Zhang F, Liu Q, Hamilton A, Wilson-O’Brien A, Ching J, Sung J, Yu J, Ng S, Kamm M, Morrison M (2021) Novel strain-level resolution of crohn’s disease mucosa-associated microbiota via an ex vivo combination of microbe culture and metagenomic sequencing. ISME J 15:1–13. https://doi.org/10.1038/s41396-021-00991-1

    Article  Google Scholar 

  • Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks

  • Waikhom L, Patgiri R (2021) Graph neural networks: methods, applications, and opportunities

  • Wali A, Ahmad M, Naseer A, Tamoor M, Gilani S (2023) Stynmedgan: Medical images augmentation using a new gan model for improved diagnosis of diseases. J Intell Fuzzy Syst (Preprint), 1–18

  • Wang C, Huang G, Huang Z, He W, et al (2023) Conditional transgan-based data augmentation for pcb electronic component inspection. Comput Intell Neurosci 2023

  • Wang F, Huang Z-A, Zhu Z, Wen Z, Zhao J, Yan G (2017) Lrlshmda: Laplacian regularized least squares for human microbe-disease association prediction. Sci Rep 7:7601. https://doi.org/10.1038/s41598-017-08127-2

    Article  Google Scholar 

  • Wu L, Cui P, Pei J, Zhao L, Guo X (2022) Graph neural networks: Foundation, frontiers and applications. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD ’22, pp. 4840–4841. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3534678.3542609

  • Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics (Oxford, England) 22:2800–5. https://doi.org/10.1093/bioinformatics/btl467

    Article  Google Scholar 

  • Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K (2019) Modeling Tabular data using Conditional GAN

  • Yang Y-Y, Lin Y-A, Chu H-M, Lin H-T (2019) Deep learning with a rethinking structure for multi-label classification

  • Yatsunenko T, Rey F, Manary M, Trehan I, Dominguez-Bello M, Contreras M, Magris M, Hidalgo G, Baldassano R, Anokhin A, Heath A, Warner B, Reeder J, Kuczynski J, Caporaso J, Lozupone C, Lauber C, Clemente J, Knights D, Gordon J (2012) Human gut microbiome viewed across age and geography. Nature 486:222–7. https://doi.org/10.1038/nature11053

    Article  Google Scholar 

  • Yogarajan V, Montiel J, Smith T, Pfahringer B (2021) Transformers for multi-label classification of medical text: an empirical comparison, pp. 114–123. https://doi.org/10.1007/978-3-030-77211-6_12

  • Zhao G, Cai Z, Wang X, Dang X (2023) Gan data augmentation methods in rock classification. Appl Sci 13(9):5316

    Article  Google Scholar 

  • Zhang W, Yang W, Lu X, Huang F, Luo F (2018) The bi-direction similarity integration method for predicting microbe-disease associations. IEEE Access PP, 1–1 https://doi.org/10.1109/ACCESS.2018.2851751

  • Zhang Z, Cui P, Zhu W (2020) Deep learning on gaphs: a survey

  • Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81. https://doi.org/10.1016/j.aiopen.2021.01.001

    Article  Google Scholar 

Download references

Funding

The authors have not received any funding support during experimentation of the work and writing of the manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, COEP Technological University (COEP Tech), Wellesley Road, Pune, 411005, Maharashtra, India

    Anushka Naik, Ishan Patwardhan & Amit Joshi

Authors
  1. Anushka Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ishan Patwardhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the work conceptualization and development of solution. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Anushka Naik.

Ethics declarations

Conflict of interest

The authors do not have Conflict of interest to declare.

Ethics approval

This study does not violate and does not involve moral and ethical statements.

Consent for publication

All authors are aware of the publication of this manuscript and agreed to its publication.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naik, A., Patwardhan, I. & Joshi, A. CGDGMDA-Net: discovering microbe-disease and drug associations through CTGAN and graph-based deep learning. Netw Model Anal Health Inform Bioinforma 13, 48 (2024). https://doi.org/10.1007/s13721-024-00484-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-024-00484-z

Keywords

Search

Navigation