MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes
Tóm tắt
Exploring the interrelationships between microbes and disease can help microbiologists make decisions and plan treatments. Predicting new microbe–disease associations currently relies on biological experiments and domain knowledge, which is time-consuming and inefficient. Automated algorithms are used to uncover the intrinsic link between microbes and disease. However, due to data noise and inadequate understanding of relevant biology, the efficient prediction of microbe–disease associations is still crucial. This study develops a multi-view graph augmentation convolutional network (MVGCNMDA) to predict potential disease-associated microbes. First, we use two data augmentation methods, edge perturbation and node dropping, to remove the data noise in the preprocessing stage. Second, we calculate Gaussian interaction profile kernel similarity and cosine similarity. Therefore, the Graph Convolutional Network(GCN) can fully use multi-view features. Then, the multi-view features are fed into the multi-attention block to learn the weights of different features adaptively. Finally, the embedding results are obtained using a Convolutional Neural Network (CNN) combiner, and the matrix completion is used to predict the relationship between potential microbes and diseases. We test our model on the Human microbe–disease Association Database (HMDAD), Disbiome, and the Combined Dataset (Peryton and MicroPhenoDB). The area under PR curve (AUPR), area under ROC curve (AUC), F1 score, and RECALL value are calculated to evaluate the performance of the developed MVGCNMDA. The AUPR is 0.9440, AUC is 0.9428, F1 score is 0.9383, and RECALL value is 0.8858. The experiments show that our model can accurately predict potential microbe–disease associations compared with the state-of-the-art works on the global Leave-One-Out-Cross-Validation (LOOCV) and the fivefold Cross-Validation (fivefold CV). To further verify the effectiveness of the proposed graph data augmentation, we designed five different settings in the ablation study. Furthermore, we present two case studies that validate the prediction of the potential association between microbes and diseases by MVGCNMDA.
Tài liệu tham khảo
Methé BA, Nelson KE, Pop M, Creasy HH, Giglio MG, Huttenhower C, Gevers D, Petrosino JF, Abubucker S, Badger JH (2012) A framework for human microbiome research. Nature 486(7402):215. https://doi.org/10.1038/nature11209
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464(7285):59–65. https://doi.org/10.1038/nature08821
Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP (2012) Human gut microbiome viewed across age and geography. Nature 486(7402):222–227. https://doi.org/10.1038/nature11053
Guarner F, Malagelada J-R (2003) Gut flora in health and disease. The Lancet 361(9356):512–519. https://doi.org/10.1016/S0140-6736(03)12489-0
David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505(7484):559–563. https://doi.org/10.1038/nature12820
Muegge BD, Kuczynski J, Knights D, Clemente JC, González A, Fontana L, Henrissat B, Knight R, Gordon JI (2011) Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332(6032):970–974. https://doi.org/10.1126/science.1198719
Dan K, Silverberg MS, Weersma RK, Gevers D, Xavier RJ (2014) Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med 6(12):107. https://doi.org/10.1186/s13073-014-0107-1
Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP (2009) A core gut microbiome in obese and lean twins. Nature 457(7228):480–484. https://doi.org/10.1038/nature07540
Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, Beaumont M, Van Treuren W, Knight R, Bell JT (2014) Human genetics shape the gut microbiome. Cell 159(4):789–799. https://doi.org/10.1016/j.cell.2014.09.053
Donia MS, Cimermancic P, Schulze CJ, Brown LCW, Martin J, Mitreva M, Clardy J, Linington RG, Fischbach MA (2014) A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell 158(6):1402–1414. https://doi.org/10.1016/j.cell.2014.08.032
Lambrecht BN, Hammad H (2015) The immunology of asthma. Nat Immunol 16(1):45–56. https://doi.org/10.1038/ni.3049
Chen C, Zhang C, Wang X, Zhang F, Zhang Z, Ma P, Feng S (2020) Helicobacter pylori infection may increase the severity of nonalcoholic fatty liver disease via promoting liver function damage, glycometabolism, lipid metabolism, inflammatory reaction and metabolic syndrome. European journal of gastroenterology & hepatology 32(7):857. https://doi.org/10.1097/MEG.0000000000001601
Vaarala O (2013) Human intestinal microbiota and type 1 diabetes. Curr Diabetes Rep 13(5):601–607. https://doi.org/10.1007/s11892-013-0409-5
Kang M, Martin A (2017) Microbiome and colorectal cancer: Unraveling host-microbiota interactions in colitis-associated colorectal cancer development. Semin Immunol 32:3–13. https://doi.org/10.1016/j.smim.2017.04.003
ShengPeng Y, Hong W (2021) Rscmda: prediction of potential mirna–disease associations based on a robust similarity constraint learning method. Interdiscip Sci Comput Life Sci 13(4):559–571. https://doi.org/10.1007/s12539-021-00459-y
Alvin ZY, Ramsey SA (2018) A computational systems biology approach for identifying candidate drugs for repositioning for cardiovascular disease. Interdiscip Sci Comput Life Sci 10(2):449–454. https://doi.org/10.1007/s12539-016-0194-3
Sangma JW, Anal SN, Pal V (2020) Clustering-based hybrid approach for identifying quantitative multidimensional associations between patient attributes, drugs and adverse drug reactions. Interdiscip Sci Comput Life Sci 12(3):237–251. https://doi.org/10.1007/s12539-020-00365-9
Ma W, Zhang L, Zeng P, Huang C, Li J, Geng B, Yang J, Kong W, Zhou X, Cui Q (2017) An analysis of human microbe-disease associations. Briefings Bioinf 18(1):85–97. https://doi.org/10.1093/bib/bbw005
Chen X, Huang Y-A, You Z-H, Yan G-Y, Wang X-S (2017) A novel approach based on katz measure to predict associations of human microbiota with non-infectious diseases. Bioinformatics 33(5):733–739. https://doi.org/10.1093/bioinformatics/btw715
Huang Z-A, Chen X, Zhu Z, Liu H, Yan G-Y, You Z-H, Wen Z (2017) Pbhmda: path-based human microbe-disease association prediction. Front Microbiol 8:233. https://doi.org/10.3389/fmicb.2017.00233
Li H, Wang Y, Jiang J, Zhao H, Feng X, Zhao B, Wang L (2019) A novel human microbe-disease association prediction method based on the bidirectional weighted network. Front Microbiol 10:676. https://doi.org/10.3389/fmicb.2019.00676
Zhang W, Lu X, Yang W, Huang F, Wang B, Wang A, Zhao Q (2018) Hngrnmf: Heterogeneous network-based graph regularized nonnegative matrix factorization for predicting events of microbe-disease associations. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 803–807 . https://doi.org/10.1109/BIBM.2018.8621085. IEEE
Wu C, Gao R, Zhang D, Han S, Zhang Y (2018) Prwhmda: Human microbe-disease association prediction by random walk on the heterogeneous network with pso. Int J Biol Sci 14(8):849–857. https://doi.org/10.7150/ijbs.24539
Luo J, Long Y (2018) Ntshmda: prediction of human microbe-disease association based on random walk by integrating network topological similarity. IEEE/ACM Trans Comput Biol Bioinform 17(4):1341–1351. https://doi.org/10.1109/TCBB.2018.2883041
Yan C, Duan G, Wu F-X, Pan Y, Wang J (2019) Brwmda: predicting microbe-disease associations based on similarities and bi-random walk on disease and microbe networks. IEEE/ACM Trans Comput Biol Bioinform 17(5):1595–1604. https://doi.org/10.1109/TCBB.2019.2907626
He B-S, Peng L-H, Li Z (2018) Human microbe-disease association prediction with graph regularized non-negative matrix factorization. Front Microbiol 9:2560. https://doi.org/10.3389/fmicb.2018.02560
Li H, Wang Y, Tan Y, Chen Z, Wang X, Pei T, Wang L (2020) Identifying microbe-disease association based on a novel back-propagation neural network model. IEEE/ACM Trans Comput Biol Bioinform 18(6):2502–2513. https://doi.org/10.1109/TCBB.2020.2986459
Liu Y, Wang S, Zhang J, Zhang W, Zhou S, Li W (2020) Dmfmda: Prediction of microbe-disease associations based on deep matrix factorization using bayesian personalized ranking. IEEE/ACM Trans Comput Biol Bioinform 18(5):1763–1772. https://doi.org/10.1109/TCBB.2020.3018138
Long Y, Luo J, Zhang Y, Xia Y (2021) Predicting human microbe-disease associations via graph attention networks with inductive matrix completion. Briefings Bioinform 22(3):146. https://doi.org/10.1093/bib/bbaa146
Dayun L, Junyi L, Yi L, Qihua H, Deng L (2021) Mgatmda: predicting microbe-disease associations via multi-component graph attention network. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2021.3116318
Ma Y, Jiang H (2021) Ninimhmda: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human microbe-disease association. Bioinformatics 36(24):5665–5671. https://doi.org/10.1093/bioinformatics/btaa1080
Long Y, Wu M, Kwoh CK, Luo J, Li X (2020) Predicting human microbe-drug associations via graph convolutional network with conditional random field. Bioinformatics 36(19):4918–4927. https://doi.org/10.1093/bioinformatics/btaa598
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(Supplement_2):779–786: https://doi.org/10.1093/bioinformatics/btaa891
Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L (2021) A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3118815
Liu F, Xue S, Wu J, Zhou C, Hu W, Paris C, Nepal S, Yang J, Yu PS (2020) Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005.08225. https://doi.org/10.24963/ijcai.2020/693
Fu H, Huang F, Liu X, Qiu Y, Zhang W (2022) Mvgcn: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks. Bioinformatics 38(2):426–434. https://doi.org/10.1093/bioinformatics/btab651
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. arXiv:1609.02907v4
Yu Z, Huang F, Zhao X, Xiao W, Zhang W (2021) Predicting drug-disease associations through layer attention graph convolutional network. Briefings Bioinform 22(4):243. https://doi.org/10.1093/bib/bbaa243
Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H (2020) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4):1241–1251. https://doi.org/10.1093/bioinformatics/btz718
Janssens Y, Nielandt J, Bronselaer A, Debunne N, Verbeke F, Wynendaele E, Van Immerseel F, Vandewynckel Y-P, De Tré G, De Spiegeleer B (2018) Disbiome database: linking the microbiome to disease. BMC Microbiol 18(1):1–6. https://doi.org/10.1186/s12866-018-1197-5
Skoufos G, Kardaras FS, Alexiou A, Kavakiotis I, Lambropoulou A, Kotsira V, Tastsoglou S, Hatzigeorgiou AG (2021) Peryton: a manual collection of experimentally supported microbe-disease associations. Nucl Acids Res 49(D1):1328–1333. https://doi.org/10.1093/nar/gkaa902
Yao G, Zhang W, Yang M, Yang H, Wang J, Zhang H, Wei L, Xie Z, Li W (2020) Microphenodb associates metagenomic data with pathogenic microbes, microbial core genes, and human disease phenotypes. Genomics Proteomics Bioinform 18(6):760–772. https://doi.org/10.1016/j.gpb.2020.11.001
Chen L, Zheng D, Liu B, Yang J, Jin Q (2016) Vfdb 2016: hierarchical and refined dataset for big data analysis–10 years on. Nucl Acids Res 44(D1):694–697. https://doi.org/10.1093/nar/gkv1239
Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Lago BA, Dave BM, Pereira S, Sharma AN, et al (2016) Card 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucl Acids Res 1004. https://doi.org/10.1093/nar/gkw1004
You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020) Graph contrastive learning with augmentations. Adv Neural Inform Process Syst 33: 5812–5823. https://doi.org/10.48550/arXiv.2010.13902
Van Laarhoven T, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21):3036–3043. https://doi.org/10.1093/bioinformatics/btr500
Sobhani I, Tap J, Roudot-Thoraval F, Roperch JP, Letulle S, Langella P, Corthier G, Van Nhieu JT, Furet JP (2011) Microbial dysbiosis in colorectal cancer (crc) patients. PloS one 6(1):16393. https://doi.org/10.1371/journal.pone.0016393
Sommer F, Bäckhed F (2013) The gut microbiota–masters of host development and physiology. Nat Rev Microbiol 11(4):227–238. https://doi.org/10.1038/nrmicro2974
Xie G, Meng T, Luo Y, Liu Z (2019) Skf-lda: similarity kernel fusion for predicting lncrna-disease association. Mol Therapy-Nucleic Acids 18:45–55. https://doi.org/10.1016/j.omtn.2019.07.022
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141. https://doi.org/10.1109/TPAMI.2019.2913372
Huang YA, You ZH, Chen X, Huang ZA, Zhang S, Yan GY (2017) Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model. J Transl Med 15(1):209. https://doi.org/10.1186/s12967-017-1304-7
Xu D, Xu H, Zhang Y, Wang M, Gao R (2021) Mdakrls: Predicting human microbe-disease association based on kronecker regularized least squares and similarities. J Trans Med 19(1) . https://doi.org/10.1186/s12967-021-02732-6
Shen X, Zhu H, Jiang X, Hu X, Yang J (2018) A novel approach based on bi-random walk to predict microbe-disease associations. In: International Conference on Intelligent Computing, pp. 746–752. https://doi.org/10.1007/978-3-319-95957-3_78. Springer
Wang F, Huang Z-A, Chen X, Zhu Z, Wen Z, Zhao J, Yan G-Y (2017) Lrlshmda: Laplacian regularized least squares for human microbe-disease association prediction. Sci Rep 7(1):1–11. https://doi.org/10.1038/s41598-017-08127-2
Sullivan A, Hunt E, MacSharry J, Murphy DM (2016) The microbiome and the pathophysiology of asthma. Respiratory Res 17(1):1–11. https://doi.org/10.1186/s12931-016-0479-4
Ver Heul A, Planer J, Kau AL (2019) The human microbiota and asthma. Clin Rev Allergy Immunol 57(3):350–363. https://doi.org/10.1007/s12016-018-8719-7
Abenavoli L, Scarpellini E, Colica C, Boccuto L, Salehi B, Sharifi-Rad J, Aiello V, Romano B, De Lorenzo A, Izzo AA (2019) Gut microbiota and obesity: a role for probiotics. Nutrients 11(11):2690. https://doi.org/10.3390/nu11112690