Quantitative Biology

, Volume 7, Issue 1, pp 17–29 | Cite as

A survey of web resources and tools for the study of TCM network pharmacology

  • Jing ZhaoEmail author
  • Jian Yang
  • Saisai Tian
  • Weidong ZhangEmail author



Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM.


In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis.


This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.


TCM network pharmacology molecular networks signaling pathways databases web servers 



This work was supported by the National Natural Science Foundation of China (Nos. 81520108030, 21472238, 61372194 and 81260672), Professor of Chang Jiang Scholars Program, Shanghai Engineering Research Center for the Preparation of Bioactive Natural Products (No. 16DZ2280200), the Scientific Foundation of Shanghai China (Nos. 13401900103 and 13401900101), the National Key Research and Development Program of China (No. 2017YFC1700200), the Natural Science Foundation of Chongqing (No. cstc2018jcyjAX0090) and Chongqing Education Reform Project of Graduate (No. yjg152017). The funders had no role in study design, data collection, analysis, decision to publish and preparation of the manuscript.


  1. 1.
    Li, S. and Zhang, B. (2013) Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin. J. Nat. Med., 11, 110–120CrossRefGoogle Scholar
  2. 2.
    Zhao, J., Jiang, P. and Zhang, W. (2010) Molecular networks for the study of TCM pharmacology. Brief. Bioinform., 11, 417–430CrossRefGoogle Scholar
  3. 3.
    Li, S., Fan T.-P., Jia, W., Lu, A. and Zhang, W. (2014) Network pharmacology in traditional Chinese medicine, evidence-based complementary and alternative medicine. Article ID 138460 Scholar
  4. 4.
    Li, P., Chen, J., Wang, J., Zhou, W., Wang, X., Li, B., Tao, W., Wang, W., Wang, Y. and Yang, L. (2014) Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases. J. Ethnopharmacol., 151, 93–107CrossRefGoogle Scholar
  5. 5.
    Huang, C., Zheng, C., Li, Y., Wang, Y., Lu, A. and Yang, L. (2014) Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief. Bioinform., 15, 710–733CrossRefGoogle Scholar
  6. 6.
    Chen, C. Y.-C. (2011) TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One, 6, e15939CrossRefGoogle Scholar
  7. 7.
    Chen, X., Zhou, H., Liu, Y. B., Wang, J. F., Li, H., Ung, C. Y., Han, L. Y., Cao, Z. W. and Chen, Y. Z. (2006) Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br. J. Pharmacol., 149, 1092–1103CrossRefGoogle Scholar
  8. 8.
    Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., et al. (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 6, 13CrossRefGoogle Scholar
  9. 9.
    Xue, R., Fang, Z., Zhang, M., Yi, Z., Wen, C. and Shi, T. (2013) TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res., 41, D1089–D1095CrossRefGoogle Scholar
  10. 10.
    Li, S., Zhang, B. and Zhang, N. (2011) Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst. Biol., 5, S10CrossRefGoogle Scholar
  11. 11.
    Lin, L., Yang, T., Fang, L., Yang, J., Yang, F. and Zhao, J. (2017) Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network. BMC Syst. Biol., 11, 121CrossRefGoogle Scholar
  12. 12.
    Sun, Y., Sheng, Z., Ma, C., Tang, K., Zhu, R., Wu, Z., Shen, R., Feng, J.,Wu, D., Huang, D., et al. (2015) Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nat. Commun., 6, 8481CrossRefGoogle Scholar
  13. 13.
    Yang, K., Bai, H., Ouyang, Q., Lai, L. and Tang, C. (2008) Finding multiple target optimal intervention in disease-related molecular network. Mol. Syst. Biol., 4, 228CrossRefGoogle Scholar
  14. 14.
    Fang, H., Wang, Y., Yang T., Ga, Y., Zhang, Y., Liu, R., Zhang, W. and Zhao, J. (2013) Bioinformatics analysis for the antirheumatic effects of Huang-Lian-Jie-Du-Tang from a network perspective. Evid-Based Compl. Alt., Article ID 245357, Scholar
  15. 15.
    Le, D. H. and Le, L. (2016) Systems pharmacology: a unified framework for prediction of drug-target interactions. Curr. Pharm. Des., 22, 3569–3575CrossRefGoogle Scholar
  16. 16.
    Fang, H.-Y., Zeng, H.-W., Lin, L.-M., Chen, X., Shen, X.-N., Fu, P., Lv, C., Liu, Q., Liu, R.-H., Zhang, W.-D., et al. (2017) A network-based method for mechanistic investigation of Shexiang Baoxin Pill’s treatment of cardiovascular diseases. Sci. Rep., 7, 43632CrossRefGoogle Scholar
  17. 17.
    Wang, T., Yang, J., Chen, X., Zhao, K., Wang, J., Zhang, Y., Zhao, J. and Ga, Y. (2017) Systems study on the antirheumatic mechanism of Tibetan medicated-bath therapy using Wuwei-Ganlu-Yaoyu-Keli. BioMed Res. Int., 2017, 2320932Google Scholar
  18. 18.
    Liang, X., Li, H. and Li, S. (2014) A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol. Biosyst., 10, 1014–1022CrossRefGoogle Scholar
  19. 19.
    Zhang, W., Tao, Q., Guo, Z., Fu, Y., Chen, X., Shar, P. A., Shahen, M., Zhu, J., Xue, J., Bai, Y., et al. (2016) Systems pharmacology dissection of the integrated treatment for cardiovascular and gastrointestinal disorders by traditional Chinese medicine. Sci. Rep., 6, 32400CrossRefGoogle Scholar
  20. 20.
    Zhou, W., Cheng, X. and Zhang, Y. (2016) Effect of Liuwei Dihuang decoction, a traditional Chinese medicinal prescription, on the neuroendocrine immunomodulation network. Pharmacol. Ther., 162, 170–178CrossRefGoogle Scholar
  21. 21.
    Ye, H., Ye, L., Kang, H., Zhang, D., Tao, L., Tang K., Liu, X., Zhu, R., Liu, Q., Chen, Y. Z. et al. (2011) HIT: linking herbal active ingredients to targets. Nucleic Acids Res., 39 (suppl_1), D1055–D1059Google Scholar
  22. 22.
    Yu, H., Chen, J., Xu, X., Li, Y., Zhao, H., Fang, Y., Li, X., Zhou, W., Wang, W. and Wang, Y. (2012) A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One, 7, e37608CrossRefGoogle Scholar
  23. 23.
    Li, Y. H., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., et al. (2018) Therapeutic target database update 2018: enriched resource for facilitating bench-toclinic research of targeted therapeutics. Nucleic Acids Res., 46, D1121–D1127Google Scholar
  24. 24.
    Whirl-Carrillo, M., McDonagh, E. M., Hebert, J. M., Gong, L., Sangkuhl, K., Thorn, C. F., Altman, R. B. and Klein, T. E. (2012) Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther., 92, 414–417CrossRefGoogle Scholar
  25. 25.
    Huang, C., Yang, Y., Chen, X., Wang, C., Li, Y., Zheng, C. and Wang, Y. (2017) Large-scale cross-species chemogenomic platform proposes a new drug discovery strategy of veterinary drug from herbal medicines. PLoS One, 12, e0184880CrossRefGoogle Scholar
  26. 26.
    Lee, A. Y., Park, W., Kang, T.-W., Cha, M. H. and Chun, J. M. (2018) Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis. J. Ethnopharmacol., 221, 151–159CrossRefGoogle Scholar
  27. 27.
    Kuhn, M., von Mering, M., Campillos, M., Jensen, L.J. and Bork, P. (2008) STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res., 36(suppl_1), D684–688Google Scholar
  28. 28.
    Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini C.A. and McKusick, V.A. (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders, Nucleic Acids Res., 33(suppl_1), D514–517CrossRefGoogle Scholar
  29. 29.
    Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z. and Woolsey, J. (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res., 34, D668–D672CrossRefGoogle Scholar
  30. 30.
    Mangal, M., Sagar, P., Singh, H., Raghava, G. P. S. and Agarwal, S. M. (2013) NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res., 41, D1124–D1129CrossRefGoogle Scholar
  31. 31.
    Tao, W., Li, B., Gao, S., Bai, Y., Shar, P. A., Zhang, W., Guo, Z., Sun, K., Fu, Y., Huang, C., et al. (2015) CancerHSP: anticancer herbs database of systems pharmacology. Sci. Rep., 5, 11481CrossRefGoogle Scholar
  32. 32.
    Zeng, X., Zhang, P., He, W., Qin, C., Chen, S., Tao, L., Wang, Y., Tan, Y., Gao, D., Wang, B., et al. (2018) NPASS: natural product activity and species source database for natural product research, discovery and tool development. Nucleic Acids Res., 46, D1217–D1222CrossRefGoogle Scholar
  33. 33.
    Fang, J., Cai, C., Wang, Q., Lin, P., Zhao, Z. and Cheng, F. (2017) Systems pharmacology-based discovery of natural products for precision oncology through targeting cancer mutated genes. CPT Pharmacometrics Syst. Pharmacol., 6, 177–187CrossRefGoogle Scholar
  34. 34.
    Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res., 46, D1074–D1082CrossRefGoogle Scholar
  35. 35.
    Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res., 42, D1083–D1090CrossRefGoogle Scholar
  36. 36.
    Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L. and Chong, J. (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 44, D1045–D1053CrossRefGoogle Scholar
  37. 37.
    Günther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E. G., Gewiess, A., Jensen, L. J., et al. (2008) SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res., 36, D919–D922CrossRefGoogle Scholar
  38. 38.
    Kumar, R., Chaudhary, K., Gupta, S., Singh, H., Kumar, S., Gautam, A., Kapoor, P., Raghava, G. P. S. and Cancer, D. R. (2013) CancerDR: cancer drug resistance database. Sci. Rep., 3, 1445CrossRefGoogle Scholar
  39. 39.
    Cotto, K. C., Wagner, A. H., Feng, Y.-Y., Kiwala, S., Coffman, A. C., Spies, G., Wollam, A., Spies, N. C., Griffith, O. L. and Griffith, M. (2018) DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res., 46, D1068–D1073CrossRefGoogle Scholar
  40. 40.
    Siramshetty, V. B., Eckert, O. A., Gohlke, B.-O., Goede, A., Chen, Q., Devarakonda, P., Preissner, S. and Preissner, R. (2018) SuperDRUG2: a one stop resource for approved/marketed drugs. Nucleic Acids Res., 46, D1137–D1143CrossRefGoogle Scholar
  41. 41.
    Yu, G., Zhang, Y., Ren, W., Dong, L., Li, J., Geng, Y., Zhang, Y., Li, D., Xu, H. and Yang, H. (2016) Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis. Int. J. Chron. Obstruct. Pulmon. Dis., 12, 85–94CrossRefGoogle Scholar
  42. 42.
    Fang, H., Wang, Y., Yang, T., Ga, Y., Zhang, Y., Liu, R., Zhang, W. and Zhao, J. (2013) Bioinformatics analysis for the antirheumatic effects of Huang-Lian-Jie-Du-Tang from a network perspective. Evid. Based Complement. Alternat. Med., 2013, 245357Google Scholar
  43. 43.
    Zhang, Y., Lin, Y., Zhao, H., Guo, Q., Yan, C. and Lin, N. (2016) Revealing the effects of the herbal pair of Euphorbia kansui and Glycyrrhiza on hepatocellular carcinoma ascites with integrating network target analysis and experimental validation. Int. J. Biol. Sci., 12, 594–606CrossRefGoogle Scholar
  44. 44.
    Okuno, Y., Tamon, A., Yabuuchi, H., Niijima, S., Minowa, Y., Tonomura, K., Kunimoto, R. and Feng, C. (2008) GLIDA: GPCR—ligand database for chemical genomics drug discovery—database and tools update, Nucleic Acids Res., 36(suppl_1), D907–D912CrossRefGoogle Scholar
  45. 45.
    Chen, X., Ji, Z. L. and Chen, Y. Z. (2002) TTD: Therapeutic Target Database. Nucleic Acids Res., 30, 412–415CrossRefGoogle Scholar
  46. 46.
    Davis, A. P., Grondin, C. J., Lennon-Hopkins, K., Saraceni-Richards, C., Sciaky, D., King, B. L., Wiegers, T. C. and Mattingly, C. J. (2015) The Comparative Toxicogenomics Database’s 10th year anniversary: update 2015. Nucleic Acids Res., 43, D914–D920CrossRefGoogle Scholar
  47. 47.
    Kanehisa, M. and Goto, S. (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28, 27–30CrossRefGoogle Scholar
  48. 48.
    Schaefer, C. F., Anthony, K., Krupa, S., Buchoff, J., Day, M., Hannay, T. and Buetow, K. H. (2009) PID: the Pathway Interaction Database. Nucleic Acids Res., 37, D674–D679Google Scholar
  49. 49.
    Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) The Reactome pathway Knowledgebase. Nucleic Acids Res., 44, D481–D487CrossRefGoogle Scholar
  50. 50.
    Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 44, D471–D480Google Scholar
  51. 51.
    Roth, B. L., Lopez, E., Patel, S. and Kroeze, W. K. (2000) The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist, 6, 252–262CrossRefGoogle Scholar
  52. 52.
    Rose, P. W., Prlić, A., Bi, C., Bluhm, W. F., Christie, C. H., Dutta, S., Green, R. K., Goodsell, D. S., Westbrook, J. D., Woo, J., et al. (2015) The RCSB Protein Data Bank: views of structural biology for basic and applied research and education. Nucleic Acids Res., 43, D345–D356Google Scholar
  53. 53.
    Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) PubChem Substance and Compound databases. Nucleic Acids Res., 44, D1202–D1213CrossRefGoogle Scholar
  54. 54.
    Carlson, H. A., Smith, R. D., Damm-Ganamet, K. L., Stuckey, J. A., Ahmed, A., Convery, M. A., Somers, D. O., Kranz, M., Elkins, P. A., Cui, G., et al. (2016) CSAR 2014: a benchmark exercise using unpublished data from pharma. J. Chem. Inf. Model., 56, 1063–1077.CrossRefGoogle Scholar
  55. 55.
    Sterling, T. and Irwin, J. J. (2015) ZINC 15–ligand discovery for everyone. J. Chem. Inf. Model., 55, 2324–2337CrossRefGoogle Scholar
  56. 56.
    Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., et al. (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res., 46, D608–D617CrossRefGoogle Scholar
  57. 57.
    Liu, Z., Guo, F., Wang, Y., Li, C., Zhang, X., Li, H., Diao, L., Gu, J., Wang, W., Li, D., et al. (2016) BATMAN-TCM: a bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine. Sci. Rep., 6, 21146CrossRefGoogle Scholar
  58. 58.
    Wang, X., Shen, Y., Wang, S., Li, S., Zhang, W., Liu, X., Lai, L., Pei, J. and Li, H. (2017) PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res., 45, W356–W360Google Scholar
  59. 59.
    Luo, H., Chen, J., Shi, L., Mikailov, M., Zhu, H., Wang, K., He, L., and Yang, L. (2011) DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome, Nucleic Acids Res., 39(suppl_2), W492–W498Google Scholar
  60. 60.
    Pereira, A. S. P., Bester, M. J. and Apostolides, Z. (2017) Exploring the anti-proliferative activity of Pelargonium sidoides DC with in silico target identification and network pharmacology. Mol. Divers., 21, 809–820CrossRefGoogle Scholar
  61. 61.
    Wei, J., Zhang, Y., Jia, Q., Liu, M., Li, D., Zhang, Y., Song, L., Hu, Y., Xian, M., Yang, H., et al. (2016) Systematic investigation of transcription factors critical in the protection against cerebral ischemia by Danhong injection. Sci. Rep., 6, 29823CrossRefGoogle Scholar
  62. 62.
    Nickel, J., Gohlke, B.-O., Erehman, J., Banerjee, P., Rong, W. W., Goede, A., Dunkel, M. and Preissner, R. (2014) SuperPred: update on drug classification and target prediction. Nucleic Acids Res., 42, W26–W31Google Scholar
  63. 63.
    Gfeller, D., Grosdidier, A., Wirth, M., Daina, A., Michielin, O. and Zoete, V. (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res., 42, W32–W38Google Scholar
  64. 64.
    Yao, Z.-J., Dong, J., Che, Y.-J., Zhu, M.-F., Wen, M., Wang, N.-N., Wang, S., Lu, A.-P. and Cao, D.-S. (2016) TargetNet: a web service for predicting potential drug-target interaction profiling via multitarget SAR models. J. Comput. Aided Mol. Des., 30, 413–424CrossRefGoogle Scholar
  65. 65.
    Hsin, K.-Y., Matsuoka, Y., Asai, Y., Kamiyoshi, K., Watanabe, T., Kawaoka, Y. and Kitano, H. (2016) systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Res., 44, W507–W513CrossRefGoogle Scholar
  66. 66.
    Hsin, K.-Y., Ghosh, S. and Kitano, H. (2013) Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One, 8, e83922CrossRefGoogle Scholar
  67. 67.
    Zsoldos, Z., Reid, D., Simon, A., Sadjad, B. S. and Johnson, A. P. (2006) eHiTS: an innovative approach to the docking and scoring function problems. Curr. Protein Pept. Sci., 7, 421–435.CrossRefGoogle Scholar
  68. 68.
    Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F. and Furlong, L. I. (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res., 45, D833–D839Google Scholar
  69. 69.
    Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., King, B. L., McMorran, R., Wiegers, J., Wiegers, T. C. and Mattingly, C. J. (2017) The Comparative Toxicogenomics Database: update 2017. Nucleic Acids Res., 45, D972–D978Google Scholar
  70. 70.
    Apweiler, R., Bairoch, A., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al. (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res., 32, D115–D119CrossRefGoogle Scholar
  71. 71.
    Landrum, M. J., Lee, J. M., Benson, M., Brown, G., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Hoover, J., et al. (2016) ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res., 44, D862–D868CrossRefGoogle Scholar
  72. 72.
    Aymé, S. and Schmidtke, J. (2007) Networking for rare diseases: a necessity for Europe. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz, 50, 1477–1483, in GermanCrossRefGoogle Scholar
  73. 73.
    MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., Junkins, H., McMahon, A., Milano, A., Morales, J., et al. (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45, D896–D901Google Scholar
  74. 74.
    Becker, K. G., Barnes, K. C., Bright, T. J. and Wang, S. A. (2004) The Genetic Association Database. Nature Genet., 36 431–432Google Scholar
  75. 75.
    Blake, J. A., Richardson, J. E., Bult, C. J., Kadin, J. A. and Eppig, J. T. (2003) MGD: the Mouse Genome Database. Nucleic Acids Res., 31, 193–195CrossRefGoogle Scholar
  76. 76.
    Twigger, S., Lu, J., Shimoyama, M., Chen, D., Pasko, D., Long, H., Ginster, J., Chen, C.-F., Nigam, R., Kwitek, A., et al. (2002) Rat Genome Database (RGD): mapping disease onto the genome. Nucleic Acids Res., 30, 125–128CrossRefGoogle Scholar
  77. 77.
    Gutiérrez-Sacristán, A., Grosdidier, S., Valverde, O., Torrens, M., Bravo, À., Piñero, J., Sanz, F. and Furlong, L. I. (2015) PsyGeNET: a knowledge platform on psychiatric disorders and their genes. Bioinformatics, 31, 3075–3077CrossRefGoogle Scholar
  78. 78.
    Robinson, P. N., Köhler, S., Bauer, S., Seelow, D., Horn, D. and Mundlos, S. (2008) The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet., 83, 610–615CrossRefGoogle Scholar
  79. 79.
    Bundschus, M., Dejori, M., Stetter, M., Tresp, V. and Kriegel, H.-P. (2008) Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, 9, 207CrossRefGoogle Scholar
  80. 80.
    Bravo, À., Piñero, J., Queralt-Rosinach, N., Rautschka, M. and Furlong, L. I. (2015) Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinformatics, 16, 55CrossRefGoogle Scholar
  81. 81.
    Rappaport, N., Twik, M., Plaschkes, I., Nudel, R., Iny Stein, T., Levitt, J., Gershoni, M., Morrey, C. P., Safran, M. and Lancet, D. (2017) MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res., 45, D877–D887Google Scholar
  82. 82.
    Roberta, A. (2007) GeneTests: integrating genetic services into patient care. Am. J. Hum. Genet., 81, 658–659CrossRefGoogle Scholar
  83. 83.
    Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X. and Jensen, L. J. (2015) DISEASES: text mining and data integration of disease-gene associations. Methods, 74, 83–89CrossRefGoogle Scholar
  84. 84.
    Allende, R. A. (2009) Accelerating searches of research grants and scientific literature with novoseekSM. Nat. Methods, 6, 394CrossRefGoogle Scholar
  85. 85.
    Safran, M., Dalah, I., Alexander, J., Rosen, N., Iny Stein, T., Shmoish, M., Nativ, N., Bahir, I., Doniger, T., Krug, H., et al. (2010) GeneCards Version 3: the human gene integrator. Database (Oxford), 2010, baq020Google Scholar
  86. 86.
    Kim, J., So, S., Lee, H.-J., Park, J. C., Kim, J. J. and Lee, H. (2013) DigSee: disease gene search engine with evidence sentences (version cancer). Nucleic Acids Res., 41, W510–W517Google Scholar
  87. 87.
    Zhang, Y., Bai, M., Zhang, B., Liu, C., Guo, Q., Sun, Y.,Wang, D., Wang, C., Jiang, Y., Lin, N., et al. (2015) Uncovering pharmacological mechanisms of Wu-tou decoction acting on rheumatoid arthritis through systems approaches: drug-target prediction, network analysis and experimental validation. Sci. Rep., 5, 9463CrossRefGoogle Scholar
  88. 88.
    Huang, W., Sherman, B. T. and Lempicki, R. A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44–57CrossRefGoogle Scholar
  89. 89.
    Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A. and Asiedu, J. K. (2017) A next generation connectivity map: L1000 platform and the first 1,000,000 profiles, Cell 171, 1437–1452. e17Google Scholar
  90. 90.
    Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A. and Ross, K.N. (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313,1929–1935Google Scholar
  91. 91.
    Wen, Z., Wang, Z., Wang, S., Ravula, R., Yang, L., Xu, J., Wang, C., Zuo, Z., Chow, M. S., Shi, L., et al. (2011) Discovery of molecular mechanisms of traditional Chinese medicinal formula Si-Wu-Tang using gene expression microarray and connectivity map. PLoS One, 6, e18278CrossRefGoogle Scholar
  92. 92.
    Lv, C., Wu, X., Wang, X., Su, J., Zeng, H., Zhao, J., Lin, S., Liu, R., Li, H., Li, X., et al. (2017) The gene expression profiles in response to 102 traditional Chinese medicine (TCM) components: a general template for research on TCMs. Sci. Rep., 7, 352CrossRefGoogle Scholar
  93. 93.
    Yoo, M., Shin, J., Kim, H., Kim, J., Kang, J. and Tan, A. C. (2018) Exploring the molecular mechanisms of traditional Chinese medicine components using gene expression signatures and connectivity map. Comput. Methods Programs Biomed.CrossRefGoogle Scholar
  94. 94.
    Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B. and Ideker, T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498–2504CrossRefGoogle Scholar
  95. 95.
    Vennix, P. P., Kuijpers, W., Tonnaer, E. L., Peters, T. A. and Ramaekers, F. C. (1990) Cytokeratins in induced epidermoid formations and cholesteatoma lesions. Arch. Otolaryngol. Head Neck Surg., 116, 560–565CrossRefGoogle Scholar
  96. 96.
    Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. and Barabási, A.-L. (2002) Hierarchical organization of modularity in metabolic networks. Science 297,1551–1555CrossRefGoogle Scholar
  97. 97.
    Padmanabhan, K., Wang, K. and Samatova, N. F. (2012) Functional annotation of hierarchical modularity. PLoS One, 7, e33744CrossRefGoogle Scholar
  98. 98.
    Kim, H. U., Ryu, J. Y., Lee, J. O. and Lee, S. Y. (2015) A systems approach to traditional oriental medicine. Nat. Biotechnol., 33, 264–268CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Interdisciplinary Complex ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
  2. 2.School of PharmacologySecond Military Medical UniversityShanghaiChina

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