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分类、本体和精准医学
Classification, Ontology, and Precision Medicine


Melissa A. Haendel ... 其他 • 2018.10.11

精准医学1的目标是对患者进行分层以改善诊断和治疗。转化研究人员正在利用更多的异质性临床数据和科学信息来创建分类策略,使干预与患者亚组中的潜在疾病机制相匹配。本体是知识的系统表示,可用于整合和分析大量异质性数据,从而可对患者进行精准分类。在本综述中,我们描述了本体及其在计算推理中的应用,以支持在诊断、治疗管理和转化研究中,对患者的精准分类。





作者信息

Melissa A. Haendel, Ph.D., Christopher G. Chute, M.D., Dr.P.H., and Peter N. Robinson, M.D.
From the Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, and the Linus Pauling Institute and the Center for Genome Research and Biocomputing, Oregon State University, Corvallis (M.A.H.); Johns Hopkins University Schools of Medicine, Public Health, and Nursing, Baltimore (C.G.C.); and the Jackson Laboratory for Genomic Medicine and the Institute for Systems Genomics, University of Connecticut — both in Farmington (P.N.R.). Address reprint requests to Dr. Robinson at the Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT 06032, or at peter.robinson@jax.org.

 

参考文献

1. National Research Council, Committee on a Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press, 2011.

2. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008;455:1061-1068.

3. Marx V. The DNA of a nation. Nature 2015;524:503-505.

4. Goroll AH. Emerging from EHR purgatory — moving from process to outcomes. N Engl J Med 2017;376:2004-2006.

5. Hyman DM, Puzanov I, Subbiah V, et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N Engl J Med 2015;373:726-736.

6. Cornet R, Chute CG. Health concept and knowledge management: twenty-five years of evolution. Yearb Med Inform 2016;Suppl 1:S32-S41.

7. Nadkarni PM, Darer JA. Migrating existing clinical content from ICD-9 to SNOMED. J Am Med Inform Assoc 2010;17:602-607.

8. Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF, eds. The description logic handbook: theory, implementation, and applications. New York: Cambridge University Press, 2003 (https://dl.acm.org/citation.cfm?id=885746).

9. National Library of Medicine. SNOMED CT. 2016 (https://www.nlm.nih.gov/healthit/snomedct).

10. Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc 2011;18:441-448.

11. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004;32(Database issue):D267-D270.

12. Chute CG. Clinical classification and terminology: some history and current observations. J Am Med Inform Assoc 2000;7:298-303.

13. Robinson PN, Bauer S. Introduction to biol-ontologies. Boca Raton, FL: CRC Press, 2011.

14. Rath A, Olry A, Dhombres F, Brandt MMC, Urbero B, Ayme S. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat 2012;33:803-808.

15. Köhler S, Vasilevsky NA, Engelstad M, et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res 2017;45(D1):D865-D876.

16. Hastings J, de Matos P, Dekker A, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2013;41(Database issue):D456-D463.

17. Bandrowski A, Brinkman R, Brochhausen M, et al. The Ontology for Biomedical Investigations. PLoS One 2016;11(4):e0154556-e0154556.

18. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res 2015;43(Database issue):D1049-D1056.

19. Fabregat A, Sidiropoulos K, Garapati P, et al. The Reactome pathway Knowledgebase. Nucleic Acids Res 2016;44(D1):D481-D487.

20. Pulteney R, Maton WG, Troilius C, von Linné C. A general view of the writings of Linnaeus. London: J. Mawman, 1805 (https://www.worldcat.org/title/general-view-of-the-writings-of-linnaeus/oclc/718424031&referer=brief_results).

21. Knibbs GH. The International classification of disease and causes of death and its revision. Med J Aust 1929;1:2-12.

22. Rea S, Pathak J, Savova G, et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform 2012;45:763-771.

23. Pathak J, Bailey KR, Beebe CE, et al. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc 2013;20(e2):e341-e348.

24. Conway M, Berg RL, Carrell D, et al. Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms. AMIA Annu Symp Proc 2011;2011:274-283.

25. Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013;20(e1):e147-e154.

26. Pathak J, Wang J, Kashyap S, et al. Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience. J Am Med Inform Assoc 2011;18:376-386.

27. Smoller JW. The use of electronic health records for psychiatric phenotyping and genomics. Am J Med Genet B Neuropsychiatr Genet 2017 May 30 (Epub ahead of print).

28. Evans DA, Cimino JJ, Hersh WR, Huff SM, Bell DS. Toward a medical-concept representation language. J Am Med Inform Assoc 1994;1:207-217.

29. Campbell KE, Cohn SP, Chute CG, Rennels G, Shortliffe EH. Gálapagos: computer-based support for evolution of a convergent medical terminology. Proc AMIA Annu Fall Symp 1996:269-273.

30. Pourzanjani A, Quisel T, Foschini L. Adherent use of digital health trackers is associated with weight loss. PLoS One 2016;11(4):e0152504-e0152504.

31. Eriksson N, Macpherson JM, Tung JY, et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet 2010;6(6):e1000993-e1000993.

32. Vasilevsky NA, Foster ED, Engelstad ME, et al. Plain-language medical vocabulary for precision diagnosis. Nat Genet 2018;50:474-476.

33. Rauch A, Wieczorek D, Graf E, et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 2012;380:1674-1682.

34. Yang Y, Muzny DM, Reid JG, et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med 2013;369:1502-1511.

35. Yang Y, Muzny DM, Xia F, et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA 2014;312:1870-1879.

36. Zhu X, Petrovski S, Xie P, et al. Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios. Genet Med 2015;17:774-781.

37. Dragojlovic N, Elliott AM, Adam S, et al. The cost and diagnostic yield of exome sequencing for children with suspected genetic disorders: a benchmarking study. Genet Med 2018 January 4 (Epub ahead of print).

38. Willig LK, Petrikin JE, Smith LD, et al. Whole-genome sequencing for identification of Mendelian disorders in critically ill infants: a retrospective analysis of diagnostic and clinical findings. Lancet Respir Med 2015;3:377-387.

39. Meng L, Pammi M, Saronwala A, et al. Use of exome sequencing for infants in intensive care units: ascertainment of severe single-gene disorders and effect on medical management. JAMA Pediatr 2017;171(12):e173438-e173438.

40. Tan TY, Dillon OJ, Stark Z, et al. Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions. JAMA Pediatr 2017;171:855-862.

41. Köhler S, Schulz MH, Krawitz P, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet 2009;85:457-464.

42. Zemojtel T, Köhler S, Mackenroth L, et al. Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci Transl Med 2014;6:252ra123-252ra123.

43. Koç A, Karaer K, Ergün MA, Cinaz P, Perçin EF. A new case of hairy elbows syndrome (hypertrichosis cubiti). Genet Couns 2007;18:325-330.

44. Winnenburg R, Bodenreider O. Coverage of phenotypes in standard terminologies. In: Proceedings of the Joint BioOntologies and BioLINK ISMB’2014 SIG session “Phenotype Day.” Bethesda, MD: National Library of Medicine, 2014:41-44.

45. Haendel MA, Balhoff JP, Bastian FB, et al. Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. J Biomed Semantics 2014;5:21-21.

46. Washington NL, Haendel MA, Mungall CJ, Ashburner M, Westerfield M, Lewis SE. Linking human diseases to animal models using ontology-based phenotype annotation. PLoS Biol 2009;7(11):e1000247-e1000247.

47. Smith CL, Eppig JT. The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data. Mamm Genome 2012;23:653-668.

48. Mungall CJ, McMurry JA, Köhler S, et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res 2016;45(D1):D718-D722. abstract (http://nar.oxfordjournals.org/content/early/2016/11/29/nar.gkw1128).

49. Meehan TF, Conte N, West DB, et al. Disease model discovery from 3,328 gene knockouts by the International Mouse Phenotyping Consortium. Nat Genet 2017;49:1231-1238.

50. Robinson PN, Köhler S, Oellrich A, et al. Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Res 2014;24:340-348.

51. Smedley D, Jacobsen JO, Jäger M, et al. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat Protoc 2015;10:2004-2015.

52. Smedley D, Schubach M, Jacobsen JOB, et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in Mendelian disease. Am J Hum Genet 2016;99:595-606.

53. Sifrim A, Popovic D, Tranchevent LC, et al. eXtasy: variant prioritization by genomic data fusion. Nat Methods 2013;10:1083-1084.

54. Singleton MV, Guthery SL, Voelkerding KV, et al. Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am J Hum Genet 2014;94:599-610.

55. Gall T, Valkanas E, Bello C, et al. Defining disease, diagnosis, and translational medicine within a homeostatic perturbation paradigm: the National Institutes of Health Undiagnosed Diseases Program experience. Front Med (Lausanne) 2017;4:62-62.

56. Greene D, Richardson S, Turro E. Phenotype similarity regression for identifying the genetic determinants of rare diseases. Am J Hum Genet 2016;98:490-499.

57. Stritt S, Nurden P, Turro E, et al. A gain-of-function variant in DIAPH1 causes dominant macrothrombocytopenia and hearing loss. Blood 2016;127:2903-2914.

58. Turro E, Greene D, Wijgaerts A, et al. A dominant gain-of-function mutation in universal tyrosine kinase SRC causes thrombocytopenia, myelofibrosis, bleeding, and bone pathologies. Sci Transl Med 2016;8:328ra30-328ra30.

59. Chute CG. Clinical data retrieval and analysis: I’ve seen a case like that before. Ann N Y Acad Sci 1992;670:133-140.

60. Lu CY, Williams MS, Ginsburg GS, Toh S, Brown JS, Khoury MJ. A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system. Genet Med 2018;20:390-396.

61. Hassanpour S, Langlotz CP. Information extraction from multi-institutional radiology reports. Artif Intell Med 2016;66:29-39.

62. Burger G, Abu-Hanna A, de Keizer N, Cornet R. Natural language processing in pathology: a scoping review. J Clin Pathol 2016 July 22 (Epub ahead of print).

63. Faden RR, Kass NE, Goodman SN, Pronovost P, Tunis S, Beauchamp TL. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. Hastings Cent Rep 2013;Spec No:S16-S27.

64. Goodman D, Johnson CO, Bowen D, Smith M, Wenzel L, Edwards K. De-identified genomic data sharing: the research participant perspective. J Community Genet 2017;8:173-181.

65. Index. FHIR, version 3.0.1 (https://www.hl7.org/fhir/).

66. Quint JK, Donaldson GC, Hurst JR, Goldring JJ, Seemungal TR, Wedzicha JA. Predictive accuracy of patient-reported exacerbation frequency in COPD. Eur Respir J 2011;37:501-507.

67. Beach WR. Patient Reported Outcomes Measurement Information System (PROMIS) may be our promise for the future. Arthroscopy 2017;33:1775-1776.

68. Chung AE, Basch EM. Incorporating the patient’s voice into electronic health records through patient-reported outcomes as the “review of systems.” J Am Med Inform Assoc 2015;22:914-916.

69. Lloyd KC, Robinson PN, MacRae CA. Animal-based studies will be essential for precision medicine. Sci Transl Med 2016;8(352):352ed12-352ed12.

70. McMurry JA, Köhler S, Washington NL, et al. Navigating the phenotype frontier: the Monarch Initiative. Genetics 2016;203:1491-1495.

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