1932

Abstract

For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: () search, retrieval, and annotation of knowledge; () data integration and analysis; () clinical decision support; and () knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.

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2018-07-20
2024-12-04
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Literature Cited

  1. 1.  Robinson PN, Bauer S 2011. Introduction to Bio-Ontologies Boca Raton, FL: CRC
    [Google Scholar]
  2. 2.  Gruber TR 1993. A translation approach to portable ontology specifications. Knowl. Acquis. 5:2199–220
    [Google Scholar]
  3. 3.  Cornet R, Chute CG 2016. Health concept and knowledge management: twenty-five years of evolution. Yearb. Med. Inform. 2016:S32–41
    [Google Scholar]
  4. 4.  Munsche H, Whitaker HA 2012. Eighteenth century classification of mental illness: Linnaeus, de Sauvages, Vogel, and Cullen. Cogn. Behav. Neurol. 25:4224–39
    [Google Scholar]
  5. 5.  Starkstein SE, Berrios GE 2015. The “preliminary discourse” to Methodical Nosology, by François Boissier de Sauvages (1772). Hist. Psychiatry 26:4477–91
    [Google Scholar]
  6. 6.  Knibbs GH 1929. The International Classification of Disease and Causes of Death and its revision. Med. J. Aust. 1:2–12
    [Google Scholar]
  7. 7. NCI (Natl. Cancer Inst.). Class: disease or disorder Term Defin., National Cancer Institute Thesaurus (NCIt) OBO Edition. http://purl.obolibrary.org/obo/NCIT_C2991
    [Google Scholar]
  8. 8. NLM (Natl. Libr. Med.). Disease Term Defin., Medical Subject Headings (MeSH). https://identifiers.org/MESH:D004194
    [Google Scholar]
  9. 9.  Robinson PN 2012. Deep phenotyping for precision medicine. Hum. Mutat. 33:5777–80
    [Google Scholar]
  10. 10.  Köhler S, Vasilevsky NA, Engelstad M, Foster E, McMurry J et al. 2017. The Human Phenotype Ontology in 2017. Nucleic Acids Res 45:D1D865–76
    [Google Scholar]
  11. 11.  Hitzler P, Krötzsch M, Parsia B, Patel-Schneider PF, Rudolph S 2012. OWL 2 web ontology language primer. Tech. Rep., World Wide Web Consort. (W3C), Dec. 11
  12. 12.  Robinson JR, Wei WQ, Roden DM, Denny JC 2018. Defining phenotypes from clinical data to drive genomic research. Annu. Rev. Biomed. Data Sci. 1: 69–92
    [Google Scholar]
  13. 13.  Mungall CJ, Gkoutos G, Smith C, Haendel M, Lewis S, Ashburner M 2010. Integrating phenotype ontologies across multiple species. Genome Biol 11:1R2
    [Google Scholar]
  14. 14.  Mulder N, Nembaware V, Adekile A, Anie KA, Inusa B et al. 2016. Proceedings of a sickle cell disease ontology workshop—towards the first comprehensive ontology for sickle cell disease. Appl. Transl. Genom. 9:23–29
    [Google Scholar]
  15. 15. WHO (World Health Organ.). History of the development of the ICD Geneva: WHO http://www.who.int/classifications/icd/en/HistoryOfICD.pdf
    [Google Scholar]
  16. 16.  Graunt J 1939. Natural and Political Observations Made Upon the Bills of Mortality WF Willcox Baltimore, MD: Johns Hopkins Univ. Press
    [Google Scholar]
  17. 17.  Cimino JJ 1998. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf. Med. 37:4–5394–403
    [Google Scholar]
  18. 18. WHO (World Health Organ.). 2016. ICD-11 revision conference report Conf. Rep 12–14 Oct Tokyo, Japan. Geneva: WHO. http://who.int/classifications/network/meeting2016/ICD-11RevisionConferenceReportTokyo.pdf
    [Google Scholar]
  19. 19.  Rodrigues J-M, Schulz S, Rector A, Spackman K, Üstün B et al. 2013. Sharing ontology between ICD 11 and SNOMED CT will enable seamless re-use and semantic interoperability. Stud. Health Technol. Inform. 192:343–46
    [Google Scholar]
  20. 20.  Rodrigues J-M, Robinson D, Della Mea V, Campbell J, Rector A et al. 2015. Semantic alignment between ICD-11 and SNOMED CT. Stud. Health Technol. Inform. 216:790–94
    [Google Scholar]
  21. 21.  Rodrigues J-M, Schulz S, Rector A, Spackman K, Millar J et al. 2014. ICD-11 and SNOMED CT Common Ontology: circulatory system. Stud. Health Technol. Inform. 205:1043–47
    [Google Scholar]
  22. 22.  Chute CG, Huff SM, Ferguson JA, Walker JM, Halamka JD 2012. There are important reasons for delaying implementation of the new ICD-10 coding system. Health Aff 31:4836–42
    [Google Scholar]
  23. 23.  Jordan EP 1932. Standard Nomenclature of Diseases and Operations New York: McGraw-Hill., 1st ed..
    [Google Scholar]
  24. 24.  Thompson ET, Hayden AC 1961. Standard Nomenclature of Diseases and Operations New York: McGraw-Hill., 5th ed..
    [Google Scholar]
  25. 25. Comm. Nomencl. Classif. Dis. 1965. Systematized Nomenclature of Pathology Skokie, IL: Coll. Am. Pathol.
    [Google Scholar]
  26. 26.  Côté RA, Sharpe WD 1976. Systematized Nomenclature of Medicine: SNOMED Skokie, IL: Coll. Am. Pathol.
    [Google Scholar]
  27. 27.  Côté RA 1993. The Systematized Nomenclature of Human and Veterinary Medicine: SNOMED International Northfield, IL: Coll. Am. Pathol.
    [Google Scholar]
  28. 28.  Evans DA, Cimino JJ, Hersh WR, Huff SM, Bell DS 1994. Toward a medical-concept representation language. The Canon Group. J. Am. Med. Inform. Assoc. 1:3207–17
    [Google Scholar]
  29. 29.  Campbell KE, Cohn SP, Chute CG, Rennels G, Shortliffe EH 1996. Gálapagos: computer-based support for evolution of a convergent medical terminology. AMIA Annu. Symp. Proc. 1996:269–73
    [Google Scholar]
  30. 30.  Campbell KE, Cohn SP, Chute CG, Shortliffe EH, Rennels G 1998. Scalable methodologies for distributed development of logic-based convergent medical terminology. Methods Inf. Med. 37:4–5426–39
    [Google Scholar]
  31. 31.  Baader F, Brandt S, Lutz C 2005. Pushing the EL envelope. Proc. Int. Jt. Conf. Artif. Intell., 19th, 30 July−5 Aug., Edinb., Scotl.364–69 San Francisco: Morgan Kaufmann
    [Google Scholar]
  32. 32.  Kudla KM, Blakemore M 2001. SNOMED takes the next step. J. AHIMA 72:762, 64–68
    [Google Scholar]
  33. 33.  Benson T 2011. The history of the Read codes: the inaugural James Read Memorial Lecture 2011. Inform. Prim. Care. 19:3173–82
    [Google Scholar]
  34. 34.  Spackman KA 2001. Normal forms for description logic expressions of clinical concepts in SNOMED RT. AMIA Annu. Symp. Proc. 2001:627–31
    [Google Scholar]
  35. 35.  Campbell JR, Talmon G, Cushman-Vokoun A, Karlsson D, Scott Campbell W 2016. An extended SNOMED CT concept model for observations in molecular genetics. AMIA Annu. Symp. Proc. 2016:352–60
    [Google Scholar]
  36. 36.  Hardiker N 2016. Harmonising ICNP and SNOMED CT: a model for effective collaboration. Stud. Health Technol. Inform. 225:744–45
    [Google Scholar]
  37. 37.  Martínez-Salvador B, Marcos M, Mañas A, Maldonado JA, Robles M 2016. Using SNOMED CT expression constraints to bridge the gap between clinical decision-support systems and electronic health records. Stud. Health Technol. Inform. 228:504–8
    [Google Scholar]
  38. 38.  Ochs C, Geller J, Perl Y, Chen Y, Xu J et al. 2015. Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies. J. Am. Med. Inform. Assoc. 22:3507–18
    [Google Scholar]
  39. 39.  Elhanan G, Ochs C, Mejino JLV Jr., Liu H, Mungall CJ, Perl Y 2017. From SNOMED CT to Uberon: transferability of evaluation methodology between similarly structured ontologies. Artif. Intell. Med. 79:9–14
    [Google Scholar]
  40. 40. NLM (Natl. Libr. Med.). 2014. 2017AB UMLS® release notes and bugs Release Doc., updated Nov. 6, 2017. https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/notes.html
    [Google Scholar]
  41. 41. NLM (Natl. Libr. Med.). 2009. Metathesaurus Fact Sheet, updated Apr. 12, 2016. https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/
    [Google Scholar]
  42. 42.  Jensen M, Cox AP, Chaudhry N, Ng M, Sule D et al. 2013. The neurological disease ontology. J. Biomed. Semant. 4:142
    [Google Scholar]
  43. 43. NLM (Natl. Libr. Med.). 2006. SPECIALIST Lexicon Fact Sheet, updated May 24, 2012. https://www.nlm.nih.gov/pubs/factsheets/umlslex.html
    [Google Scholar]
  44. 44. NLM (Natl. Libr. Med.). The UMLS Semantic Network Fact Sheet, updated Oct. 5, 2015. https://semanticnetwork.nlm.nih.gov/
    [Google Scholar]
  45. 45.  McCray AT 2003. An upper-level ontology for the biomedical domain. Comp. Funct. Genom. 4:180–84
    [Google Scholar]
  46. 46.  Pecina P, Dušek O, Goeuriot L, Hajič J, Hlaváčová J et al. 2014. Adaptation of machine translation for multilingual information retrieval in the medical domain. Artif. Intell. Med. 61:3165–85
    [Google Scholar]
  47. 47.  Merabti T, Abdoune H, Letord C, Sakji S, Joubert M, Darmoni SJ 2011. Mapping the ATC classification to the UMLS metathesaurus: some pragmatic applications. Stud. Health Technol. Inform. 166:206–13
    [Google Scholar]
  48. 48.  Ceusters W, Smith B, Goldberg L 2005. A terminological and ontological analysis of the NCI Thesaurus. Methods Inf. Med. 44:4498–507
    [Google Scholar]
  49. 49.  Jensen MA, Ferretti V, Grossman RL, Staudt LM 2017. The NCI Genomic Data Commons as an engine for precision medicine. Blood 130:4453–59
    [Google Scholar]
  50. 50. NCI (Natl. Cancer Inst.). NCIt Neoplasm Core terminology files File Release, accessed 6 Mar. 2018 NCI Rockville, MD: https://evs.nci.nih.gov/ftp1/NCI_Thesaurus/Neoplasm/About_Core.html
    [Google Scholar]
  51. 51. NCI (Natl. Cancer Inst.). NCI Term Browser Terminol. Resour. https://ncit.nci.nih.gov/ncitbrowser/pages/multiple_search.jsf?nav_type=terminologies
    [Google Scholar]
  52. 52.  Min H, Zheng L, Perl Y, Halper M, De Coronado S, Ochs C 2017. Relating complexity and error rates of ontology concepts: more complex NCIt concepts have more errors. Methods Inf. Med. 56:3200–8
    [Google Scholar]
  53. 53.  Mougin F, Bodenreider O 2008. Auditing the NCI thesaurus with semantic web technologies. AMIA Annu. Symp. Proc. 2008:500–4
    [Google Scholar]
  54. 54.  Jiang G, Solbrig HR, Chute CG 2012. Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups. J. Am. Med. Inform. Assoc. 19:e129–36
    [Google Scholar]
  55. 55.  Min H, Mobahi H, Irvin K, Avramovic S, Wojtusiak J 2017. Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology. J. Biomed. Semant. 8:139
    [Google Scholar]
  56. 56.  Bertaud Gounot V, Donfack V, Lasbleiz J, Bourde A, Duvauferrier R 2011. Creating an ontology driven rules base for an expert system for medical diagnosis. Stud. Health Technol. Inform. 169:714–18
    [Google Scholar]
  57. 57.  Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC et al. 2017. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49:2170–74
    [Google Scholar]
  58. 58.  Smith B, Ashburner M, Rosse C, Bard J, Bug W et al. 2007. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol. 25:111251–55
    [Google Scholar]
  59. 59. NCI (Natl. Cancer Inst.). NCIt OBO edition File Release. https://github.com/NCI-Thesaurus/thesaurus-obo-edition
    [Google Scholar]
  60. 60.  Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM et al. 2016. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J. Biomed. Semant. 7:144
    [Google Scholar]
  61. 61.  Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA 2012. Uberon, an integrative multi-species anatomy ontology. Genome Biol 13:1R5
    [Google Scholar]
  62. 62.  Davies SC 2017. Annual report of the Chief Medical Officer 2016: generation genome Annu. Rep U.K. Dep. Health London: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/631043/CMO_annual_report_generation_genome.pdf
    [Google Scholar]
  63. 63.  Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A 2015. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res 43:D789–98
    [Google Scholar]
  64. 64.  Pavan S, Rommel K, Mateo Marquina ME, Höhn S, Lanneau V, Rath A 2017. Clinical practice guidelines for rare diseases: the Orphanet database. PLOS ONE 12:1e0170365
    [Google Scholar]
  65. 65.  Bragin E, Chatzimichali EA, Wright CF, Hurles ME, Firth HV et al. 2014. DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation. Nucleic Acids Res 42:D993–1000
    [Google Scholar]
  66. 66.  Groza T, Köhler S, Moldenhauer D, Vasilevsky N, Baynam G et al. 2015. The Human Phenotype Ontology: semantic unification of common and rare disease. Am. J. Hum. Genet. 97:1111–24
    [Google Scholar]
  67. 67.  Rodriguez JC, González GA, Fresno C, Llera AS, Fernández EA 2016. Improving information retrieval in functional analysis. Comput. Biol. Med. 79:10–20
    [Google Scholar]
  68. 68.  Liaw S-T, Taggart J, Yu H 2014. EHR-based disease registries to support integrated care in a health neighbourhood: an ontology-based methodology. Stud. Health Technol. Inform. 205:171–75
    [Google Scholar]
  69. 69.  Mao Y, Lu Z 2017. MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank. J. Biomed. Semant. 8:115
    [Google Scholar]
  70. 70.  Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL et al. 2013. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J. Am. Med. Inform. Assoc. 20:e147–54
    [Google Scholar]
  71. 71.  Conway M, Berg RL, Carrell D, Denny JC, Kho AN et al. 2011. Analyzing the heterogeneity and complexity of electronic health record oriented phenotyping algorithms. AMIA Annu. Symp. Proc. 2011:274–83
    [Google Scholar]
  72. 72.  Wei W-Q, Bastarache LA, Carroll RJ, Marlo JE, Osterman TJ et al. 2017. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLOS ONE 12:7e0175508
    [Google Scholar]
  73. 73.  Chen W, Kowatch R, Lin S, Splaingard M, Huang Y 2015. Interactive cohort identification of sleep disorder patients using natural language processing and i2b2. Appl. Clin. Inform. 6:2345–63
    [Google Scholar]
  74. 74.  Cui L, Bozorgi A, Lhatoo SD, Zhang G-Q, Sahoo SS 2012. EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. AMIA Annu. Symp. Proc. 2012:1191–200
    [Google Scholar]
  75. 75.  Banda JM, Seneviratune M, Hernandez-Boussard T, Shah NH 2018. Advances in electronic phenotyping: from rule-based definitions to machine learning models. Annu. Rev. Biomed. Data Sci. 1:53–68
    [Google Scholar]
  76. 76.  Zhang Y-F, Gou L, Zhou T-S, Lin D-N, Zheng J et al. 2017. An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management. J. Biomed. Inform. 72:45–59
    [Google Scholar]
  77. 77.  Rosier A, Mabo P, Temal L, Van Hille P, Dameron O et al. 2016. Remote monitoring of cardiac implantable devices: ontology driven classification of the alerts. Stud. Health Technol. Inform. 221:59–63
    [Google Scholar]
  78. 78.  Wunsch G, da Costa CA, Righi RR 2017. A semantic-based model for triage patients in emergency departments. J. Med. Syst. 41:465
    [Google Scholar]
  79. 79.  Alexiou A, Psiha M, Vlamos P 2015. Towards an expert system for accurate diagnosis and progress monitoring of Parkinson's disease. Adv. Exp. Med. Biol. 822:151–64
    [Google Scholar]
  80. 80.  Carletti G, Giuliodori P, Di Pietri V, Peretti A, Amenta F 2016. An ontology-based consultation system to support medical care on board seagoing vessels. Int. Marit. Health 67:114–20
    [Google Scholar]
  81. 81.  Maurice P, Dhombres F, Blondiaux E, Friszer S, Guilbaud L et al. 2017. Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology. J. Gynecol. Obstet. Hum. Reprod. 46:5423–29
    [Google Scholar]
  82. 82.  Abidi S 2017. A knowledge-modeling approach to integrate multiple clinical practice guidelines to provide evidence-based clinical decision support for managing comorbid conditions. J. Med. Syst. 41:12193
    [Google Scholar]
  83. 83.  Lin Y, Staes CJ, Shields DE, Kandula V, Welch BM, Kawamoto K 2015. Design, development, and initial evaluation of a terminology for clinical decision support and electronic clinical quality measurement. AMIA Annu. Symp. Proc. 2015:843–51
    [Google Scholar]
  84. 84.  Köhler S, Doelken SC, Ruef BJ, Bauer S, Washington N et al. 2013. Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Research 2:30
    [Google Scholar]
  85. 85.  Smedley D, Oellrich A, Köhler S, Ruef B, Sanger Mouse Genet. Proj., et al. 2013. PhenoDigm: analyzing curated annotations to associate animal models with human diseases. Database 2013:bat025
    [Google Scholar]
  86. 86.  Bauer S, Köhler S, Schulz MH, Robinson PN 2012. Bayesian ontology querying for accurate and noise-tolerant semantic searches. Bioinformatics 28:192502–8
    [Google Scholar]
  87. 87.  Köhler S, Schulz MH, Krawitz P, Bauer S, Dölken S et al. 2009. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am. J. Hum. Genet. 85:4457–64
    [Google Scholar]
  88. 88.  Zemojtel T, Köhler S, Mackenroth L, Jäger M, Hecht J et al. 2014. Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci. Transl. Med. 6:252252ra123
    [Google Scholar]
  89. 89.  Smedley D, Jacobsen JOB, Jäger M, Köhler S, Holtgrewe M et al. 2015. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat. Protoc. 10:122004–15
    [Google Scholar]
  90. 90.  Bone WP, Washington NL, Buske OJ, Adams DR, Davis J et al. 2016. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genet. Med. 18:6608–17
    [Google Scholar]
  91. 91.  Singleton MV, Guthery SL, Voelkerding KV, Chen K, Kennedy B et al. 2014. Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am. J. Hum. Genet. 94:4599–610
    [Google Scholar]
  92. 92.  Posey JE, Harel T, Liu P, Rosenfeld JA, James RA et al. 2017. Resolution of disease phenotypes resulting from multilocus genomic variation. N. Engl. J. Med. 376:121–31
    [Google Scholar]
  93. 93.  Philippakis AA, Azzariti DR, Beltran S, Brookes AJ, Brownstein CA et al. 2015. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum. Mutat. 36:10915–21
    [Google Scholar]
  94. 94. Health Level Seven Int. HL7 implementation guide for CDA® release 2: consolidated CDA templates for clinical notes Prod. Brief, updated Feb. 6, 2018, Ann Arbor, MI. http://www.hl7.org/implement/standards/product_brief.cfm?product_id=379
    [Google Scholar]
  95. 95.  Boyce RD, Ryan PB, Norén GN, Schuemie MJ, Reich C et al. 2014. Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest. Drug Saf 37:8557–67
    [Google Scholar]
  96. 96.  Chakrabarti S, Sen A, Huser V, Hruby GW, Rusanov A et al. 2017. An interoperable similarity-based cohort identification method using the OMOP common data model version 5.0. Int. J. Healthc. Inf. Syst. Inform. 1:11–18
    [Google Scholar]
  97. 97. Clin. Cancer Genome Task Team Glob. Alliance Genom. Health. 2017. Sharing clinical and genomic data on cancer—the need for global solutions. N. Engl. J. Med. 376:212006–9
    [Google Scholar]
  98. 98.  Kock-Schoppenhauer A-K, Kamann C, Ulrich H, Duhm-Harbeck P, Ingenerf J 2017. Linked data applications through ontology based data access in clinical research. Stud. Health Technol. Inform. 235:131–35
    [Google Scholar]
  99. 99.  Nussbaum RL, Rehm HL 2015. ClinGen and genetic testing: Drs. Nussbaum and Rehm reply. N. Engl. J. Med. 373:141379
    [Google Scholar]
  100. 100.  Schulz S, Spackman K, James A, Cocos C, Boeker M 2011. Scalable representations of diseases in biomedical ontologies. J. Biomed. Semant. 2:S6
    [Google Scholar]
  101. 101.  Mungall CJ, Koehler S, Robinson P, Holmes I, Haendel M 2016. k-BOOM: A Bayesian approach to ontology structure inference, with applications in disease ontology construction. bioRxiv 048843. https://doi.org/10.1101/048843
    [Crossref]
  102. 102.  Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C et al. 2017. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res 45:D1D712–22
    [Google Scholar]
  103. 103.  Faria D, Jiménez-Ruiz E, Pesquita C, Santos E, Couto FM 2014. Towards annotating potential incoherences in BioPortal mappings. The Semantic Web: ISWC 2014 P Mika, T Tudorache, A Bernstein, C Welty, C Knoblock et al.1732 Lect. Notes Comput. Sci. 8797 Cham, Switz.: Springer Int.
    [Google Scholar]
  104. 104.  Banchereau R, Hong S, Cantarel B, Baldwin N, Baisch J et al. 2016. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165:3551–65
    [Google Scholar]
  105. 105. Natl. Res. Counc. 2011. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease Washington, DC: Natl. Acad. Press
    [Google Scholar]
  106. 106.  Clare AJ, Wicky HE, Empson RM, Hughes SM 2017. RNA-sequencing analysis reveals a regulatory role for transcription factor Fezf2 in the mature motor cortex. Front. Mol. Neurosci. 10:283
    [Google Scholar]
  107. 107.  Eidsaa M, Stubbs L, Almaas E 2017. Comparative analysis of weighted gene co-expression networks in human and mouse. PLOS ONE 12:11e0187611
    [Google Scholar]
  108. 108.  Menche J, Guney E, Sharma A, Branigan PJ, Loza MJ et al. 2017. Integrating personalized gene expression profiles into predictive disease-associated gene pools. NPJ Syst. Biol. Appl. 3:10
    [Google Scholar]
  109. 109.  Yu G, Wang L-G, Yan G-R, He Q-Y 2015. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31:4608–9
    [Google Scholar]
  110. 110.  LePendu P, Musen MA, Shah NH 2011. Enabling enrichment analysis with the Human Disease Ontology. J. Biomed. Inform. 44:S31–38
    [Google Scholar]
  111. 111.  Ibn-Salem J, Köhler S, Love MI, Chung H-R, Huang N et al. 2014. Deletions of chromosomal regulatory boundaries are associated with congenital disease. Genome Biol 15:9423
    [Google Scholar]
  112. 112.  McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT et al. 2010. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28:5495–501
    [Google Scholar]
  113. 113.  Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA et al. 2015. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47:6569–76
    [Google Scholar]
  114. 114.  Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP et al. 2016. Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation. J. Biomed. Semant. 7:8
    [Google Scholar]
  115. 115.  Opap K, Mulder N 2017. Recent advances in predicting gene−disease associations. F1000Research 6:578
    [Google Scholar]
  116. 116.  Hu Y, Zhou W, Ren J, Dong L, Wang Y et al. 2016. Annotating the function of the human genome with Gene Ontology and Disease Ontology. Biomed Res. Int. 2016:4130861
    [Google Scholar]
  117. 117.  Forrey AW, McDonald CJ, DeMoor G, Huff SM, Leavelle D et al. 1996. Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. Clin. Chem. 42:181–90
    [Google Scholar]
  118. 118.  Maritz R, Aronsky D, Prodinger B 2017. The International Classification of Functioning, Disability and Health (ICF) in electronic health records: a systematic literature review. Appl. Clin. Inform. 8:3964–80
    [Google Scholar]
  119. 119.  Mitchell JA, Loughman WD, Epstein CJ 1980. GENFILES: a computerized medical genetics information network. II. MEDGEN: the clinical genetics system. Am. J. Med. Genet. 7:3251–66
    [Google Scholar]
  120. 120.  Jouhet V, Mougin F, Bréchat B, Thiessard F 2017. Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus. J. Biomed. Semant. 8:16
    [Google Scholar]
  121. 121.  Malone J, Holloway E, Adamusiak T, Kapushesky M, Zheng J et al. 2010. Modeling sample variables with an Experimental Factor Ontology. Bioinformatics 26:81112–18
    [Google Scholar]
  122. 122.  Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E et al. 2015. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43:D1071–78
    [Google Scholar]
  123. 123.  Hayman GT, Laulederkind SJF, Smith JR, Wang S-J, Petri V et al. 2016. The Disease Portals, disease–gene annotation and the RGD disease ontology at the Rat Genome Database. Database 2016:baw034
    [Google Scholar]
  124. 124. APA (Am. Psychiatr. Assoc.). 2013. Diagnostic and Statistical Manual of Mental Disorders Arlington, VA: APA, 5th ed..
    [Google Scholar]
  125. 125.  Cowell LG, Smith B 2010. Infectious Disease Ontology. Infectious Disease Informatics V Sintchenko 373–95 New York: Springer-Verlag
    [Google Scholar]
  126. 126.  Gordon CL, Pouch S, Cowell LG, Boland MR, Platt HL et al. 2013. Design and evaluation of a bacterial clinical infectious diseases ontology. AMIA Annu. Symp. Proc. 2013:502–11
    [Google Scholar]
  127. 127.  Younesi E, Malhotra A, Gündel M, Scordis P, Kodamullil AT et al. 2015. PDON: Parkinson's disease ontology for representation and modeling of the Parkinson's disease knowledge domain. Theor. Biol. Med. Model. 12:120
    [Google Scholar]
  128. 128.  Mugzach O, Peleg M, Bagley SC, Guter SJ, Cook EH, Altman RB 2015. An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data. J. Biomed. Inform. 56:333–47
    [Google Scholar]
  129. 129.  McCray AT, Trevvett P, Frost HR 2014. Modeling the autism spectrum disorder phenotype. Neuroinformatics 12:2291–305
    [Google Scholar]
  130. 130.  Fisher HM, Hoehndorf R, Bazelato BS, Dadras SS, King LE Jr et al. 2016. DermO; an ontology for the description of dermatologic disease. J. Biomed. Semant. 7:38
    [Google Scholar]
  131. 131.  Takatsuki T, Saito M, Kumagai S, Takayama E, Ohshima K et al. A RDF-based portal of biological phenotype data created in Japan Presented at Int. Semant. Web Conf., , 15th., Kobe, Japan:
    [Google Scholar]
  132. 132.  Daughton AR, Priedhorsky R, Fairchild G, Generous N, Hengartner A et al. 2017. An extensible framework and database of infectious disease for biosurveillance. BMC Infect. Dis. 17:1549
    [Google Scholar]
  133. 133.  Barton A, Barton A, Rosier A, Rosier A, Burgun A, Ethier J-F 2014. The cardiovascular disease ontology. Formal Ontology in Information Systems P Garbacz, O Kutz 40914 Front. Artif. Intell. Appl. 267 Amsterdam: IOS
    [Google Scholar]
  134. 134.  Ceusters W, Smith B 2010. Foundations for a realist ontology of mental disease. J. Biomed. Semant. 1:110
    [Google Scholar]
  135. 135.  Schleyer TK, Ruttenberg A, Duncan W, Haendel M, Torniai C et al. 2013. An ontology-based method for secondary use of electronic dental record data. AMIA Jt. Summits Transl. Sci. Proc. 2013:234–38
    [Google Scholar]
  136. 136.  Keerthikumar S, Raju R, Kandasamy K, Hijikata A, Ramabadran S et al. 2009. RAPID: Resource of Asian Primary Immunodeficiency Diseases. Nucleic Acids Res 37:D863–67
    [Google Scholar]
  137. 137.  Wang L, Li M, Xie J, Cao Y, Liu H, He Y 2017. Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China. Sci. Rep. 7:113819
    [Google Scholar]
  138. 138.  Fu X, Batista-Navarro R, Rak R, Ananiadou S 2015. Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows. J. Biomed. Semant. 6:8
    [Google Scholar]
  139. 139.  Lin Y, Xiang Z, He Y 2011. Brucellosis Ontology (IDOBRU) as an extension of the Infectious Disease Ontology. J. Biomed. Semant. 2:19
    [Google Scholar]
  140. 140.  Lin Y, Sakamoto N 2009. Ontology driven modeling for the knowledge of genetic susceptibility to disease. Kobe J. Med. Sci. 54:6E290–303
    [Google Scholar]
  141. 141.  Porter JF, Kingsland LC3rd, Lindberg DA, Shah I, Benge JM et al. 1988. The AI/RHEUM knowledge-based computer consultant system in rheumatology. Performance in the diagnosis of 59 connective tissue disease patients from Japan. Arthritis Rheum 31:2219–26
    [Google Scholar]
  142. 142.  Ryerson CJ, Corte TJ, Lee JS, Richeldi L, Walsh SLF et al. 2017. A standardized diagnostic ontology for fibrotic interstitial lung disease: an international working group perspective. Am. J. Respir. Crit. Care Med. 196:124954
    [Google Scholar]
  143. 143.  Mizuno S, Ogishima S, Nishigori H, Jamieson DG, Verspoor K et al. 2016. The Pre-Eclampsia Ontology: a disease ontology representing the domain knowledge specific to pre-eclampsia. PLOS ONE 11:10e0162828
    [Google Scholar]
  144. 144.  Chen Q, Wu J, Li S, Lyu P, Wang Y, Li M 2016. An ontology-driven, case-based clinical decision support model for removable partial denture design. Sci. Rep. 6:27855
    [Google Scholar]
  145. 145.  Joseph S, Barai RS, Bhujbalrao R, Idicula-Thomas S 2016. PCOSKB: A KnowledgeBase on genes, diseases, ontology terms and biochemical pathways associated with PolyCystic Ovary Syndrome. Nucleic Acids Res 44:D1D1032–35
    [Google Scholar]
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