Research article Special Issues

PMO: A knowledge representation model towards precision medicine

  • These authors contributed to this work equally
  • Received: 31 December 2019 Accepted: 18 May 2020 Published: 08 June 2020
  • With the rapid development of biomedical technology, amounts of data in the field of precision medicine (PM) are growing exponentially. Valuable knowledge is included in scattered data in which meaningful biomedical entities and their semantic relationships are buried. Therefore, it is necessary to develop a knowledge representation model like ontology to formally represent the relationships among diseases, phenotypes, genes, mutations, drugs, etc. and achieve effective integration of heterogeneous data. On basis of existing work, our study focus on solving the following issues: (ⅰ) Selecting the primary entities in PM domain; (ⅱ) collecting and integrating biomedical vocabularies related to the above entities; (ⅲ) defining and normalizing semantic relationships among these entities. We proposed a semi-automated method which improved the original Ontology Development 101 method to build the Precision Medicine Ontology (PMO), including defining the scope of the PMO according to the definition of PM, collecting terms from different biomedical resources, integrating and normalizing the terms by a combination of machine and manual work, defining the annotation properties, reusing existing ontologies and taxonomies, defining semantic relationships, evaluating PMO and creating the PMO website. Finally, the Precision Medicine Vocabulary (PMV) contains 4.53 million terms collected from 62 biomedical vocabularies, and the PMO includes eleven branches of PM concepts such as disease, chemical and drug, phenotype, gene, mutation, gene product and cell, described by 93 semantic relationships among them. PMO is an open, extensible ontology of PM, all of the terms and relationships in which could be obtained from the PMO website (http://www.phoc.org.cn/pmo/). Compared to existing project, our work has brought a broader and deeper coverage of mutation, gene and gene product, which enriches the semantic type and vocabulary in PM domain and benefits all users in terms of medical literature annotation, text mining and knowledge base construction.

    Citation: Li Hou, Meng Wu, Hongyu Kang, Si Zheng, Liu Shen, Qing Qian, Jiao Li. PMO: A knowledge representation model towards precision medicine[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 4098-4114. doi: 10.3934/mbe.2020227

    Related Papers:

    [1] Eunha Shim, Beth Kochin, Alison Galvani . Insights from epidemiological game theory into gender-specific vaccination against rubella. Mathematical Biosciences and Engineering, 2009, 6(4): 839-854. doi: 10.3934/mbe.2009.6.839
    [2] Hyun Mo Yang, André Ricardo Ribas Freitas . Biological view of vaccination described by mathematical modellings: from rubella to dengue vaccines. Mathematical Biosciences and Engineering, 2019, 16(4): 3195-3214. doi: 10.3934/mbe.2019159
    [3] Lili Liu, Xi Wang, Yazhi Li . Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity. Mathematical Biosciences and Engineering, 2023, 20(12): 20914-20938. doi: 10.3934/mbe.2023925
    [4] Pannathon Kreabkhontho, Watchara Teparos, Thitiya Theparod . Potential for eliminating COVID-19 in Thailand through third-dose vaccination: A modeling approach. Mathematical Biosciences and Engineering, 2024, 21(8): 6807-6828. doi: 10.3934/mbe.2024298
    [5] Eunha Shim . Optimal strategies of social distancing and vaccination against seasonal influenza. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1615-1634. doi: 10.3934/mbe.2013.10.1615
    [6] Majid Jaberi-Douraki, Seyed M. Moghadas . Optimal control of vaccination dynamics during an influenza epidemic. Mathematical Biosciences and Engineering, 2014, 11(5): 1045-1063. doi: 10.3934/mbe.2014.11.1045
    [7] Xunyang Wang, Canyun Huang, Yuanjie Liu . A vertically transmitted epidemic model with two state-dependent pulse controls. Mathematical Biosciences and Engineering, 2022, 19(12): 13967-13987. doi: 10.3934/mbe.2022651
    [8] Hamed Karami, Pejman Sanaei, Alexandra Smirnova . Balancing mitigation strategies for viral outbreaks. Mathematical Biosciences and Engineering, 2024, 21(12): 7650-7687. doi: 10.3934/mbe.2024337
    [9] Lan Zou, Jing Chen, Shigui Ruan . Modeling and analyzing the transmission dynamics of visceral leishmaniasis. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1585-1604. doi: 10.3934/mbe.2017082
    [10] Hai-Feng Huo, Tian Fu, Hong Xiang . Dynamics and optimal control of a Zika model with sexual and vertical transmissions. Mathematical Biosciences and Engineering, 2023, 20(5): 8279-8304. doi: 10.3934/mbe.2023361
  • With the rapid development of biomedical technology, amounts of data in the field of precision medicine (PM) are growing exponentially. Valuable knowledge is included in scattered data in which meaningful biomedical entities and their semantic relationships are buried. Therefore, it is necessary to develop a knowledge representation model like ontology to formally represent the relationships among diseases, phenotypes, genes, mutations, drugs, etc. and achieve effective integration of heterogeneous data. On basis of existing work, our study focus on solving the following issues: (ⅰ) Selecting the primary entities in PM domain; (ⅱ) collecting and integrating biomedical vocabularies related to the above entities; (ⅲ) defining and normalizing semantic relationships among these entities. We proposed a semi-automated method which improved the original Ontology Development 101 method to build the Precision Medicine Ontology (PMO), including defining the scope of the PMO according to the definition of PM, collecting terms from different biomedical resources, integrating and normalizing the terms by a combination of machine and manual work, defining the annotation properties, reusing existing ontologies and taxonomies, defining semantic relationships, evaluating PMO and creating the PMO website. Finally, the Precision Medicine Vocabulary (PMV) contains 4.53 million terms collected from 62 biomedical vocabularies, and the PMO includes eleven branches of PM concepts such as disease, chemical and drug, phenotype, gene, mutation, gene product and cell, described by 93 semantic relationships among them. PMO is an open, extensible ontology of PM, all of the terms and relationships in which could be obtained from the PMO website (http://www.phoc.org.cn/pmo/). Compared to existing project, our work has brought a broader and deeper coverage of mutation, gene and gene product, which enriches the semantic type and vocabulary in PM domain and benefits all users in terms of medical literature annotation, text mining and knowledge base construction.





    [1] K. Hudson, R. Lifton, B. Patrick-Lake, E. G. Burchard, T. Coles, R. Collins, et al., The precision medicine initiative cohort program - Building a Research Foundation for 21st Century Medicine, Precis. Med. Initiative Work. Group Rep. Advis. Comm. Dir., 2015 (2015).
    [2] G. S. Ginsburg, K. A. Phillips, Precision medicine: from science to value, Health Aff., 37 (2018), 694-701. doi: 10.1377/hlthaff.2017.1624
    [3] M. A. Haendel, C. G. Chute, P. N. Robinson, Classification, Ontology, and Precision Medicine, N. Engl. J. Med., 379 (2018), 1452-1462. doi: 10.1056/NEJMra1615014
    [4] O. Bodenreider, The Unified Medical Language System (UMLS): Integrating biomedical terminology, Nucleic Acids Res., 32 (2004), D267-270. doi: 10.1093/nar/gkh061
    [5] A. T. McCray, An upper-level ontology for the biomedical domain, Comp. Funct. Genomics, 4 (2003), 80-84. doi: 10.1002/cfg.255
    [6] C. G. Chute, Clinical classification and terminology: Some history and current observations, J. Am. Med. Inform. Assoc., 7 (2000), 298-303. doi: 10.1136/jamia.2000.0070298
    [7] N. F. Noy, D. L. McGuinness, Ontology development 101: A guide to creating your first ontology, Stanford Knowledge Systems Laboratory Technical Report, 2001. Available from: http://www.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness-abstract.html.
    [8] R. Hoehndorf, P. N. Schofield, G. V. Gkoutos, The role of ontologies in biological and biomedical research: a functional perspective, Brief. Bioinform., 16 (2015), 1069-1080. doi: 10.1093/bib/bbv011
    [9] M. Martinez-Romero, C. Jonquet, M. J. O'Connor, J. Graybeal, A. Pazos, M. A. Musen, NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation, J. Biomed. Semantics, 8 (2017), 21. doi: 10.1186/s13326-017-0128-y
    [10] The Gene Ontology Consortium, Expansion of the Gene Ontology knowledgebase and resources, Nucleic Acids Res., 45 (2017), D331-D338.
    [11] W. A. Kibbe, C. Arze, V. Felix, E. Mitraka, E. Bolton, G. Fu, et al., Disease Ontology 2015 update: An expanded and updated database of human diseases for linking biomedical knowledge through disease data, Nucleic Acids Res., 43 (2015), D1071-D1078. doi: 10.1093/nar/gku1011
    [12] S. Köhler, N. A. Vasilevsky, M. Engelstad, E. Foster, J. McMurry, S. Aymé, et al., The Human Phenotype Ontology in 2017, Nucleic Acids Res., 45 (2017), D865-D876. doi: 10.1093/nar/gkw1039
    [13] Y. Lin, S. Mehta, H. Küçük-McGinty, J. P. Turner, D. Vidovic, M. Forlin, et al., Drug target ontology to classify and integrate drug discovery data, J. Biomed. Semantics, 8 (2017), 50. doi: 10.1186/s13326-017-0161-x
    [14] J. Huang, K. Eilbeck, B. Smith, J. A. Blake, D. Dou, W. Huang, et al., The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology, J. Biomed. Semantics, 7 (2016), 24. doi: 10.1186/s13326-016-0066-0
    [15] N. S. Tawfik, M. R. Spruit, PreMedOnto: A Computer Assisted Ontology for Precision Medicine, in Natural Language Processing and Information Systems (eds. E. Métais, F. Meziane, S. Vadera, V. Sugumaran and M. Saraee), Springer, (2019), 329-336.
    [16] Bioportal, Precision Medicine Ontology, 2020. Available from: https://bioportal.bioontology.org/ontologies/PREMEDONTO/?p=classes&conceptid=root.
    [17] Y. He, E. Ong, J. Schaub, F. Dowd, J. F. O'toole, A. Siapos, et al., OPMI: The Ontology of Precision Medicine and Investigation and its support for clinical data and metadata representation and analysis, The 10th International Conference on Biomedical Ontology (ICBO-2019), 2019. Available from: https://drive.google.com/file/d/1TN3jH4hoh40Saa8adlR_TocREGTNPVlC/view.
    [18] M. Uschold, M. Gruninger, Ontologies: Principles, methods and applications, Knowl. Eng. Rev., 11 (1996), 93-136. doi: 10.1017/S0269888900007797
    [19] K. Knight, I. Chancer, M. Haines, V. Hatzivassiloglou, E. Hovy, M. Iida, et al., Filling knowledge gaps in a broad-coverage MT system, The 14th International Joint Conference on Artificial Intelligence, 1995. Available from: https://www.ijcai.org/Proceedings/95-2/Papers/048.pdf.
    [20] A. Bernaras, I. Laresgoiti, J. Corera, Building and reusing ontologies for electrical network applications, The 12th European Conference on Artificial Intelligence, 1996. Available from: https://www.tib.eu/en/search/id/BLCP%3ACN015300062/Building-and-Reusing-Ontologies-for-Electrical/.
    [21] B. Peraketh, C. Menzel, R. J. Mayer, F. Fillion, M. T. Futrell, P. S. DeWitte, et al., Ontology Capture Method (IDEF5), Knowledge Based Systems, Incorporated Technical report, 1994. Available from: https://apps.dtic.mil/dtic/tr/fulltext/u2/a288442.pdf.
    [22] M. F. Lopez, A. Gomez-Perez, J. P. Sierra, A. P. Sierra, Building a chemical ontology using methontology and the ontology design environment, IEEE Intell. Syst. App., 14 (1999), 37-46.
    [23] M. Gruninger, M. S. Fox, Methodology for the design and evaluation of ontologies, Workshop on Basic Ontological Issues in Knowledge Sharing, International Joint Conference on Artificial Intelligence, 1995. Available from: https://www.semanticscholar.org/paper/Methodology-for-the-Design-and-Evaluation-of-Gruninger/497abc0ddace6a7772a5f5a3edb3d7b751476755.
    [24] M. A. Musen, T. Protege, The Protege Project: A Look Back and a Look Forward, AI Matters, 1 (2015), 4-12.
    [25] M. N. Asim, M. Wasim, M. U. G. Khan, W. Mahmood, H. M. Abbasi, A survey of ontology learning techniques and applications, Database (Oxford), 2018 (2018), bay101.
    [26] M. Cristani, R. Cuel, A Survey on Ontology Creation Methodologies, Int. J. Semantic Web Inf. Syst., 1(2005), 49-69. doi: 10.4018/jswis.2005040103
    [27] National Research Council, Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease, National Academies Press, 2011.
    [28] National Institutes of Health, All of Us Research Program, 2020. Available from: https://allofus.nih.gov/.
    [29] M. Murphy, G. Brown, C. Wallin, T. Tatusova, K. Pruitt, T. Murphy, et al., Gene Help: Integrated Access to Genes of Genomes in the Reference Sequence Collection, National Center for Biotechnology Information. 2019. Available from: https://www.ncbi.nlm.nih.gov/books/NBK3841/.
    [30] M. J. Landrum, J. M. Lee, M. Benson, G. Brown, C. Chao, S. Chitipiralla, et al., ClinVar: Public archive of interpretations of clinically relevant variants, Nucleic Acids Res., 44 (2016), D862-D868. doi: 10.1093/nar/gkv1222
    [31] A. T. McCray, S. Srinivasan, A. C. Browne, Lexical methods for managing variation in biomedical terminologies, Proc. Annu. Symp. Comput. Appl. Med. Care, 1994 (1994), 235-239.
    [32] The Gene Ontology Consortium, The Gene Ontology Resource: 20 years and still GOing strong, Nucleic Acids Res., 47 (2019), D330-D338.
    [33] R. Winnenburg, L. Rodriguez, F. Callaghan, A. Sorbello, A. Szarfman, Aligning pharmacologic classes between MeSH and ATC, International Conference on Biomedical Ontology (ICBO), 2013. Available from: http://ceur-ws.org/Vol-1061/Paper5_vdos2013.pdf.
    [34] QIAGEN, Relationships, 2020. Available from: http://qiagen.force.com/KnowledgeBase/KnowledgeIPAPage?id=kA41i000000L5pCCAS.
    [35] A. Kramer, J. Green, J. Pollard, S. Tugendreich, Causal analysis approaches in Ingenuity Pathway Analysis, Bioinformatics, 30 (2014), 523-530. doi: 10.1093/bioinformatics/btt703
    [36] R. C. Jackson, J. P. Balhoff, E. Douglass, N. L. Harris, C. J. Mungall, J. A. Overton, ROBOT: A Tool for Automating Ontology Workflows, BMC Bioinformatics, 20 (2019), 407-417. doi: 10.1186/s12859-019-3002-3
    [37] The OBO foundry, Principles: Overview, 2020. Available from: http://www.obofoundry.org/principles/fp-000-summary.html.
    [38] H. Sun, P. Deng, J. Li, L. Shen, Q. Qian, Automatic Concept Update Strategy Towards Heterogeneous Terminology Integration, Data Anal. Knowl. Discov., 4(2020), 121-130.
    [39] B. Smith, M. Ashburner, C. Rosse, J. Bard, W. Bug, W. Ceusters, et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration, Nat. Biotechnol., 25 (2007), 1251-1255. doi: 10.1038/nbt1346
    [40] PMapp, Chinese Program of Precision Medicine: Construction of Precision Medicine Knowledgebase for Disease Research, 2020. Available from: http://pmap.org.cn/.
    [41] A. Gangemi, C. Catenacci, M. Ciaramita, J. Lehmann, A theoretical framework for ontology evaluation and validation, Semantic Web Applications and Perspectives (SWAP), 2005. Available from: https://www.academia.edu/download/58656915/9.pdf.
    [42] P. Cimiano, J. P. McCrae, P. Buitelaar, Lexicon Model for Ontologies: Community Report, W3C, 2016 (2016).
    [43] A. Isaac, E. Summers, SKOS Simple Knowledge Organization System Reference, W3C, 7 (2009).
  • This article has been cited by:

    1. Impact of vaccine arrival on the optimal control of a newly emerging infectious disease: A theoretical study, 2012, 9, 1551-0018, 539, 10.3934/mbe.2012.9.539
    2. Bruno Buonomo, Modeling ITNs Usage: Optimal Promotion Programs Versus Pure Voluntary Adoptions, 2015, 3, 2227-7390, 1241, 10.3390/math3041241
    3. Hamadjam Abboubakar, Jean Claude Kamgang, Leontine Nkague Nkamba, Daniel Tieudjo, Bifurcation thresholds and optimal control in transmission dynamics of arboviral diseases, 2018, 76, 0303-6812, 379, 10.1007/s00285-017-1146-1
    4. Benjamin Riche, Hélène Bricout, Marie-Laure Kürzinger, Sylvain Roche, Jean Iwaz, Jean-François Etard, René Ecochard, Modeling and predicting the long-term effects of various strategies and objectives of varicella-zoster vaccination campaigns, 2016, 15, 1476-0584, 927, 10.1080/14760584.2016.1183483
    5. Lingcai Kong, Jinfeng Wang, Weiguo Han, Zhidong Cao, Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model, 2016, 13, 1660-4601, 253, 10.3390/ijerph13030253
    6. Nkengafac Villyen Motaze, Zinhle E. Mthombothi, Olatunji Adetokunboh, C. Marijn Hazelbag, Enrique M. Saldarriaga, Lawrence Mbuagbaw, Charles Shey Wiysonge, The Impact of Rubella Vaccine Introduction on Rubella Infection and Congenital Rubella Syndrome: A Systematic Review of Mathematical Modelling Studies, 2021, 9, 2076-393X, 84, 10.3390/vaccines9020084
    7. BRUNO BUONOMO, ON THE OPTIMAL VACCINATION STRATEGIES FOR HORIZONTALLY AND VERTICALLY TRANSMITTED INFECTIOUS DISEASES, 2011, 19, 0218-3390, 263, 10.1142/S0218339011003853
    8. Bruno Buonomo, 2014, Chapter 3, 978-3-319-06922-7, 23, 10.1007/978-3-319-06923-4_3
    9. Chairat Modnak, Jin Wang, Zindoga Mukandavire, Simulating optimal vaccination times during cholera outbreaks, 2014, 07, 1793-5245, 1450014, 10.1142/S1793524514500144
    10. Drew Posny, Jin Wang, Zindoga Mukandavire, Chairat Modnak, Analyzing transmission dynamics of cholera with public health interventions, 2015, 264, 00255564, 38, 10.1016/j.mbs.2015.03.006
    11. Matt J. Keeling, Andrew Shattock, Optimal but unequitable prophylactic distribution of vaccine, 2012, 4, 17554365, 78, 10.1016/j.epidem.2012.03.001
    12. Bruno Buonomo, Cruz Vargas-De-León, Effects of Mosquitoes Host Choice on Optimal Intervention Strategies for Malaria Control, 2014, 132, 0167-8019, 127, 10.1007/s10440-014-9894-z
    13. Bruno Buonomo, Piero Manfredi, Alberto d’Onofrio, Optimal time-profiles of public health intervention to shape voluntary vaccination for childhood diseases, 2019, 78, 0303-6812, 1089, 10.1007/s00285-018-1303-1
    14. Adison Thongtha, Chairat Modnak, Optimal COVID-19 epidemic strategy with vaccination control and infection prevention measures in Thailand, 2022, 7, 24680427, 835, 10.1016/j.idm.2022.11.002
    15. Calvin Tadmon, Arnaud Feukouo Fossi, Berge Tsanou, A two–strain avian–human influenza model with environmental transmission: Stability analysis and optimal control strategies, 2024, 10075704, 107981, 10.1016/j.cnsns.2024.107981
    16. Gui Guan, Zhenyuan Guo, Yanyu Xiao, Dynamical behaviors of a network-based SIR epidemic model with saturated incidence and pulse vaccination, 2024, 137, 10075704, 108097, 10.1016/j.cnsns.2024.108097
    17. Samiullah Salim, Fazal Dayan, Muhammad Azizur Rehman, Husam A. Neamah, Optimization and Control in Rubella Transmission Dynamics: A Boundedness-Preserving Numerical Model with Vaccination, 2024, 23529148, 101595, 10.1016/j.imu.2024.101595
    18. Habtamu Ayalew Engida, Demeke Fisseha, Malaria and leptospirosis co-infection: A mathematical model analysis with optimal control and cost-effectiveness analysis, 2025, 24682276, e02517, 10.1016/j.sciaf.2024.e02517
    19. Giovanni Ziarelli, Stefano Pagani, Nicola Parolini, Francesco Regazzoni, Marco Verani, A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts, 2025, 437, 00457825, 117796, 10.1016/j.cma.2025.117796
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5789) PDF downloads(299) Cited by(4)

Article outline

Figures and Tables

Figures(3)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog