PMO: A knowledge representation model towards precision medicine

Li Hou; Meng Wu; Hongyu Kang; Si Zheng; Liu Shen; Qing Qian; Jiao Li; Li Hou; Meng Wu; Hongyu Kang; Si Zheng; Liu Shen; Qing Qian; Jiao Li

doi:10.3934/mbe.2020227

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 4098-4114. doi: 10.3934/mbe.2020227

Previous Article Next Article

Research article Special Issues

PMO: A knowledge representation model towards precision medicine

Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
^† 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.
- biomedical ontology,
- precision medicine,
- semantic web,
- controlled vocabulary,
- taxonomy
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:

Abstract

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.

References

[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).

Reader Comments

Your name:*

Email:*
© 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)