PMO: A knowledge representation model towards precision medicine
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2020-05-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2020227?viewType=HTML |
| _version_ | 1818737643791319040 |
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| author | Li Hou Meng Wu Hongyu Kang Si Zheng Liu Shen Qing Qian Jiao Li |
| author_facet | Li Hou Meng Wu Hongyu Kang Si Zheng Liu Shen Qing Qian Jiao Li |
| author_sort | Li Hou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-18T00:56:19Z |
| format | Article |
| id | doaj.art-db2e5fa0cc4740de8464d843b817f4c4 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-18T00:56:19Z |
| publishDate | 2020-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-db2e5fa0cc4740de8464d843b817f4c42022-12-21T21:26:30ZengAIMS PressMathematical Biosciences and Engineering1551-00182020-05-011744098411410.3934/mbe.2020227PMO: A knowledge representation model towards precision medicineLi Hou 0Meng Wu 1Hongyu Kang2Si Zheng3Liu Shen4Qing Qian5Jiao Li 6Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, ChinaWith 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.https://www.aimspress.com/article/doi/10.3934/mbe.2020227?viewType=HTMLbiomedical ontologyprecision medicinesemantic webcontrolled vocabularytaxonomy |
| spellingShingle | Li Hou Meng Wu Hongyu Kang Si Zheng Liu Shen Qing Qian Jiao Li PMO: A knowledge representation model towards precision medicine Mathematical Biosciences and Engineering biomedical ontology precision medicine semantic web controlled vocabulary taxonomy |
| title | PMO: A knowledge representation model towards precision medicine |
| title_full | PMO: A knowledge representation model towards precision medicine |
| title_fullStr | PMO: A knowledge representation model towards precision medicine |
| title_full_unstemmed | PMO: A knowledge representation model towards precision medicine |
| title_short | PMO: A knowledge representation model towards precision medicine |
| title_sort | pmo a knowledge representation model towards precision medicine |
| topic | biomedical ontology precision medicine semantic web controlled vocabulary taxonomy |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2020227?viewType=HTML |
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