Volume 31 Issue 2
Mar.  2022
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WANG Yueyue, LEI Xiujuan, PAN Yi. Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning[J]. Chinese Journal of Electronics, 2022, 31(2): 345-353. doi: 10.1049/cje.2020.00.212
Citation: WANG Yueyue, LEI Xiujuan, PAN Yi. Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning[J]. Chinese Journal of Electronics, 2022, 31(2): 345-353. doi: 10.1049/cje.2020.00.212

Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

doi: 10.1049/cje.2020.00.212
Funds:  This work was supported by the National Natural Science Foundation of China (61672334, 61972451, 61902230) and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK201901010)
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  • Author Bio:

    is currently working toward M.S. degree in Shaanxi Normal University, Xi’an, China. Her current research interests include bioinformatics, data mining and deep learning. (Email: yueyuewang@snnu.edu.cn)

    (corresponding author) is a Professor in the School of Computer Science at Shaanxi Normal University, Xi’an, China. She received the M.S. and Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China, in 2001 and 2005, respectively. Her current research interests mainly include intelligent computing and bioinformatics. (Email: xjlei@snnu.edu.cn)

    is currently a Professor of the Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He has served as Chair of Computer Science Department at Georgia State University during 2005-2020. He received the B.E. and M.E. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and the Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies. (Email: yipan@gsu.edu)

  • Received Date: 2020-07-16
  • Accepted Date: 2021-08-03
  • Available Online: 2021-10-08
  • Publish Date: 2022-03-05
  • Numerous microbes inhabit human body, making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study, we develop a prediction method by learning global graph feature on the heterogeneous network (called HNGFL). Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple similarities. Based on microbe Gaussian interaction profile (GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.
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  • [1]
    B. A. Methe, K. E. Nelson, M. Pop, et al., “A framework for human microbiome research,” Nature, vol.486, no.7402, pp.215–221, 2012. doi: 10.1038/nature11209
    [2]
    E. Thursby and N. Juge, “Introduction to the human gut microbiota,” Biochem J, vol.474, no.11, pp.1823–1836, 2017. doi: 10.1042/BCJ20160510
    [3]
    F. Sommer and F. Backhed, “The gut microbiota−masters of host development and physiology,” Nat Rev Microbiol, vol.11, no.4, pp.227–238, 2013. doi: 10.1038/nrmicro2974
    [4]
    A. A. Althani, H. E. Marei, W. S. Hamdi, et al., “Human microbiome and its association with health and diseases,” Journal of Cellular Physiology, vol.231, no.8, pp.1688–1694, 2016. doi: 10.1002/jcp.25284
    [5]
    C. Huttenhower, D. Gevers, R. Knight, et al., “Structure, function and diversity of the healthy human microbiome,” Nature, vol.486, no.7402, pp.207–214, 2012. doi: 10.1038/nature11234
    [6]
    L. Wen, R. E. Ley, P. Y. Volchkov, et al., “Innate immunity and intestinal microbiota in the development of Type 1 diabetes,” Nature, vol.455, no.7216, pp.1109–1113, 2008. doi: 10.1038/nature07336
    [7]
    T. Eom, Y. S. Kim, C. H. Choi, et al., “Current understanding of microbiota- and dietary-therapies for treating inflammatory bowel disease,” Journal of Microbiology, vol.56, no.3, pp.189–198, 2018. doi: 10.1007/s12275-018-8049-8
    [8]
    F. V. Nimwegen, J. Penders, E. E. Stobberingh, et al., “Mode and place of delivery, gastrointestinal microbiota, and their influence on asthma and atopy,” Journal of Allergy & Clinical Immunology, vol.128, no.5, pp.948–955.e3, 2011.
    [9]
    W. Ma, L. Zhang, P. Zeng, et al., “An analysis of human microbe-disease associations,” Brief Bioinform, vol.18, no.1, pp.85–97, 2017. doi: 10.1093/bib/bbw005
    [10]
    X. Chen, Y. A. Huang, Z. H. You, et al., “A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases,” Bioinformatics, vol.33, no.5, pp.733–739, 2017.
    [11]
    H. Li, Y. Wang, J. Jiang, et al., “A novel human microbe-disease association prediction method based on the bidirectional weighted network,” Frontiers in Microbiology, vol.10, article no.676, 2019. doi: 10.3389/fmicb.2019.00676
    [12]
    X. J. Lei and Y. Y. Wang, “Predicting microbe-disease association by learning graph representations and rule-based inference on the heterogeneous network,” Frontiers in Microbiology, vol.11, article no.579, 2020. doi: 10.3389/fmicb.2020.00579
    [13]
    S. Zhou, J. P. Zhang, and Z. P. Zhang, “A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network,” Plos One, vol.12, no.9, article no.e0184394, 2017. doi: 10.1371/journal.pone.0184394
    [14]
    W. Fan, Z. A. Huang, X. Chen, et al., “LRLSHMDA: Laplacian regularized least squares for human microbe-disease association prediction,” Scientific Reports, vol.7, no.1, article no.7601, 2017. doi: 10.1038/s41598-017-08127-2
    [15]
    J. Qu, Y. Zhao, and J. Yin, “Identification and analysis of human microbe-disease associations by matrix decomposition and label propagation,” Frontiers in Microbiology, vol.10, no.291, 2019.
    [16]
    C. Y. Fan, X. J. Lei, L. Guo, et al., “Predicting the associations between microbes and diseases by integrating multiple data sources and path-based HeteSim scores,” Neurocomputing, vol.323, pp.76–85, 2019. doi: 10.1016/j.neucom.2018.09.054
    [17]
    Y. Long and J. Luo, “WMGHMDA: A novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network,” BMC Bioinformatics, vol.20, no.1, article no.541, 2019. doi: 10.1186/s12859-019-3066-0
    [18]
    W. Zhang, W. T. Yang, X. T. Lu, et al., “The bi-direction similarity integration method for predicting microbe-disease associations,” IEEE Access, vol.6, pp.38052–38061, 2018. doi: 10.1109/ACCESS.2018.2851751
    [19]
    B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp.701–710, 2014.
    [20]
    D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp.1225–1234, 2016.
    [21]
    Z. Ye, H. Zhao, Y. Zhu, et al., “HSNR: A network representation learning algorithm using hierarchical structure embedding,” Chinese Journal of Electronics, vol.29, no.6, pp.1141–1152, 2020. doi: 10.1049/cje.2020.10.001
    [22]
    A. Grover and J. Leskovec, “Node2vec: Scalable feature learning for networks,” The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp.855–864, 2016.
    [23]
    S. Cao, W. Lu, and Q. Xu, “GraRep: Learning graph representations with global structural information,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia, pp.891–900, 2015.
    [24]
    T. Joachims, “A support vector method for multivariate performance measures,” in Proceedings of the 22nd international conference on Machine learning, Bonn, Germany, pp.377–384, 2005.
    [25]
    C. E. Lipscomb, “Medical subject headings (MeSH),” Bull Med Libr Assoc, vol.88, no.3, pp.265–266, 2000.
    [26]
    X. Z. Zhou, J. Menche, A. L. Barabasi, et al., “Human symptoms-disease network,” Nature Communications, vol.5, article no.4212, 2014.
    [27]
    Y. W. Niu, C. Q. Qu, G. H. Wang, et al., “RWHMDA: Random walk on hypergraph for microbe-disease association prediction,” Frontiers in Microbiology, vol.10, article no.1578, 2019. doi: 10.3389/fmicb.2019.01578
    [28]
    M. A. Friedl and C. E. Brodley, “Decision tree classification of land cover from remotely sensed data,” Remote Sensing of Environment, vol.61, no.3, pp.399–409, 1997.
    [29]
    J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol.29, no.5, pp.1189–1232, 2001. doi: 10.1214/aos/1013203450
    [30]
    L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol.4, no.2, article no.1883, 2009. doi: 10.4249/scholarpedia.1883
    [31]
    A. Liaw and M. Wiener, “Classification and regression by randomForest,” The R News, vol.2/3, pp.18–22, 2002.
    [32]
    Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol.55, no.1, pp.119–139, 1997. doi: 10.1006/jcss.1997.1504
    [33]
    C. Yan, G. H. Duan, F. X. Wu, et al., “BRWMDA: Predicting microbe-disease associations based on similarities and bi-random walk on disease and microbe networks,” IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol.17, no.5, pp.1595–1604, 2020.
    [34]
    J. W. Luo and Y. H. Long, “NTSHMDA: Prediction of human microbe-disease association based on random walk by integrating network topological similarity,” IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol.17, no.4, pp.1341–1351, 2020.
    [35]
    Y. A. Huang, Z. H. You, X. Chen, et al., “Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model,” Journal of Translational Medicine, vol.15, article no.209, 2017.
    [36]
    S. Li, M. Xie, and X. Liu, “A novel approach based on bipartite network recommendation and KATZ model to predict potential micro-disease associations,” Frontiers in Genetics, vol.10, article no.1147, 2019. doi: S.Li,M.Xie,andX.Liu
    [37]
    W. Bao, Z. Jiang, and D. S. Huang, “Novel human microbe-disease association prediction using network consistency projection,” BMC Bioinformatics, vol.18, article no.543, 2017. doi: 10.1186/s12859-017-1968-2
    [38]
    M. Ng, T. Fleming, M. Robinson, et al., “Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the global burden of disease study 2013,” Lancet, vol.384, no.9945, pp.766–81, 2014. doi: 10.1016/S0140-6736(14)60460-8
    [39]
    R. E. Ley, “Obesity and the human microbiome,” Curr Opin Gastroenterol, vol.26, no.1, pp.5–11, 2010. doi: 10.1097/MOG.0b013e328333d751
    [40]
    H. Lin, Y. An, and H. Tang, “Alterations of bile acids and gut microbiota in obesity induced by high fat diet in rat model,” Journal of Agricultural and Food Chemistry, vol.67, no.13, pp.3624–3632, 2019. doi: 10.1021/acs.jafc.9b00249
    [41]
    J. Long, Q. Cai, M. Steinwandel, et al., “Association of oral microbiome with type 2 diabetes risk,” J Periodontal Res, vol.52, no.3, pp.636–643, 2017. doi: 10.1111/jre.12432
    [42]
    K. M. Rood, I. A. Buhimschi, J. A. Jurcisek, et al., “Skin microbiota in obese women at risk for surgical site infection after cesarean delivery,” Scientific Reports, vol.8, no.1, article no.8756, 2018. doi: 10.1038/s41598-018-27134-5
    [43]
    N. A. Mervish, J. Hu, L. A. Hagan, et al., “Associations of the oral microbiota with obesity and menarche in inner city girls,” J Child Obes, vol.4, no.1, article no.2, 2019. doi: 10.21767/2572-5394.100068
    [44]
    E. Isolauri, “Microbiota and obesity,” Nestle Nutr Inst Workshop Ser, vol.88, pp.95–105, 2017.
    [45]
    A. Jemal, F. Bray, M. M. Center, et al., “Global cancer statistics,” CA Cancer J Clin, vol.61, no.2, pp.69–90, 2011. doi: 10.3322/caac.20107
    [46]
    W. E. Moore and L. H. Moore, “Intestinal floras of populations that have a high risk of colon cancer,” Applied & Environmental Microbiology, vol.61, no.9, pp.3202–3207, 1995.
    [47]
    L. M. T. Dicks, L. S. Mikkelsen, E. Brandsborg, et al., “Clostridium difficile, the difficult “Kloster” fuelled by antibiotics,” Curr Microbiol, vol.76, no.6, pp.774–782, 2019. doi: 10.1007/s00284-018-1543-8
    [48]
    J. E. Bader, R. T. Enos, K. T. Velázquez, et al., “Macrophage depletion using clodronate liposomes decreases tumorigenesis and alters gut microbiota in the AOM/DSS mouse model of colon cancer,” Am J Physiol Gastrointest Liver Physiol, vol.314, no.1, pp.G22–G31, 2018. doi: 10.1152/ajpgi.00229.2017
    [49]
    M. Geravand and P. Fallah, “Investigation of enterococcus faecalis population in patients with polyp and colorectal cancer in comparison of healthy individuals,” Arquivos de Gastroenterologia, vol.56, no.2, pp.141–145, 2019. doi: 10.1590/s0004-2803.201900000-28
    [50]
    B. Flemer, D. B. Lynch, J. M. Brown, et al., “Tumour-associated and non-tumour-associated microbiota in colorectal cancer,” Gut, vol.66, no.4, pp.633–643, 2017. doi: 10.1136/gutjnl-2015-309595
    [51]
    W. C. Knowler, E. Barrett-Connor, S. E. Fowler, et al., “Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin,” N Engl J Med, vol.346, no.6, pp.393–403, 2002. doi: 10.1056/NEJMoa012512
    [52]
    J. Tuomilehto, J. Lindstrm, J. G. Eriksson, et al., “Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance,” New England Journal of Medicine, vol.344, no.18, pp.1343–1350, 2001. doi: 10.1056/NEJM200105033441801
    [53]
    E. A. Babaev, I. P. Balmasova, A. M. Mkrtumyan, et al., “Metagenomic analysis of gingival sulcus microbiota and pathogenesis of periodontitis associated with type 2 diabetes mellitus,” Bulletin of Experimental Biology and Medicine, vol.163, no.6, pp.718–721, 2017. doi: 10.1007/s10517-017-3888-6
    [54]
    A. Bharti, S. P. S. Chawla, S. Kumar, et al., “Asymptomatic bacteriuria among the patients of type 2 diabetes mellitus,” J Family Med Prim Care, vol.8, no.2, pp.539–543, 2019. doi: 10.4103/jfmpc.jfmpc_403_18
    [55]
    L. Egshatyan, D. Kashtanova, A. Popenko, et al., “Gut microbiota and diet in patients with different glucose tolerance,” Endocr Connect, vol.5, no.1, pp.1–9, 2016. doi: 10.1530/EC-15-0094
    [56]
    L. Sun, C. Xie, G. Wang, et al., “Gut microbiota and intestinal FXR mediate the clinical benefits of metformin,” Nat Med, vol.24, no.12, pp.1919–1929, 2018. doi: 10.1038/s41591-018-0222-4
    [57]
    M. Osiri, T. Tantawichien, and U. Deesomchock, “Edwardsiella tarda bacteremia and septic arthritis in a patient with diabetes mellitus,” Southeast Asian J Trop Med Public Health, vol.28, no.3, pp.669–672, 1997.
    [58]
    G. Liu, L. Liang, G. Yu, et al., “Pumpkin polysaccharide modifies the gut microbiota during alleviation of type 2 diabetes in rats,” Int J Biol Macromol, vol.115, pp.711–717, 2018. doi: 10.1016/j.ijbiomac.2018.04.127
    [59]
    Y. Pan, X. Lei, and Y. Zhang, “Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach,” Medicinal Research Reviews, vol.42, no.1, pp.441–461, 2022. doi: 10.1002/med.21847
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