Presentation about semantic annotation of biomedical data. Presented at LIRMM, INRIA and other between 2008 and 2010.

1. Semantic annotation of biomedical data Clement Jonquet jonquet@stanford.edu INRIA - EXMO seminar - March 24th, 2010

3. Ontology-based annotation workflow: concept recognition, semantic expansion, why it’s hard?

4. Annotation services: the NCBO Annotator web service, the NCBO biomedical resources index

5. Users & use cases

6. Conclusion and future work2 INRIA - EXMO seminar - March 24th, 2010

8. Web content must be semantically described using ontologies

9. Semantic annotations help to structure the web

10. Annotation is not an easy task

11. Automatic vs. manual

12. Lack of annotation tools (convenient, simple to use and easily integrated into automatic processes)

13. Today’s web content (& public data available through the web) mainly composed of unstructured textINRIA - EXMO seminar - March 24th, 2010 3

15. Getting access to all is hard: formats, locations, APIs

16. Lack of tools that easily access all ontologies (domain)

17. Users do not always know the structure of an ontology’s content or how to use it in order to do the annotations themselves

18. Lack of tools to do the annotations automatically

19. Boring additional task without immediate reward for the userINRIA - EXMO seminar - March 24th, 2010 4

21. Very diverse, grow very fast

22. Most of the data are unstructured and rarely described with ontology concepts available in the domains

23. Hard for biomedical researchers to find the data they need

24. Data integration problem

25. Translational discoveries are prevented

26. Good example of use of ontologies and terminologies for annotations

27. Gene Ontology annotations

28. PubMed (biomedical literature) indexed with Mesh headings

29. Limitations

30. UMLS only, almost nothing for OBO & OWL ontologies

31. Manual approaches, curators (scalability?)

32. Automatic approaches (usability & accuracy?) INRIA - EXMO seminar - March 24th, 2010 5

34. Large scale – to scale up for many resources and ontologies

35. Automatic – to keep precision and accuracy

36. Easy to use and to access – to prevent the biomedical community from getting lost

37. Customizable – to fit very specific needs

38. Smart – to leverage the knowledge contained in ontologiesINRIA - EXMO seminar - March 24th, 2010 6

40. a dataset, clinical-trial description, research article, imaging study

41. Text metadata = the set of free text that describe or ‘annotate’ an element

42. Resource = a collection of elements

43. GEO, PubMed, ClinicalTrial.gov, Guideline.gov, ArrayExpress

44. Concept = a unique entity (class) in an specific ontology (has an URI)

45. UMLS CUI or NCBO URI e.g., C0025202, DOID:1909

46. Term = a string that identifies a given concept (name, synonyms)

47. Melanoma, Melanomas, Malignant melanoma

48. Annotation = meta-information on a data: this data deals with this concept

49. PMID17984116 deals with C0025202INRIA - EXMO seminar - March 24th, 2010 7

50. Why using ontologies? They structure the knowledge from a domain They specify terms that can be used by natural language processing algorithms to process text They uniquely identify concept (URI) They specify relations between concepts that can be used for computing concept similarity They define hierarchies allowing abstraction of type They play the role of common denominator for various data from a domain INRIA - EXMO seminar - March 24th, 2010 8

51. Why using ontologies? 9 INRIA - EXMO seminar - March 24th, 2010

53. May involve NLP, stemming, spell-checking, or recognition of morphological variants

54. Concept disambiguation

55. Scalability issues

56. We want to deal with millions of concepts (~4M)

57. 200+ ontologies in several format, spread out

58. Huge biomedical resources e.g., PubMed 17M citations

59. What to do with annotations when the ontologies and the resources evolve over time

60. e.g., elements in resources are added

61. e.g., concepts in ontologies are removed INRIA - EXMO seminar - March 24th, 2010 10

62. Why is it a hard problem? (2/2) How to leverage the knowledge contained in ontologies? Process the transitive closure for relations (not trivial for ontologies with 300k concepts) Execute semantic distance algorithms to determine similarity Compute mappings between ontologies to connect ontologies one another Keep all of this up to date when ontologies evolve e.g., new GO version everyday INRIA - EXMO seminar - March 24th, 2010 11

63. Ontology-based annotation workflow INRIA - EXMO seminar - March 24th, 2010` 12 First, direct annotations are created by recognizing concepts in raw text, Second, annotations are semantically expanded using knowledge of the ontologies, Third, all annotations are scored according to the context in which they have been created.

65. 220 ontologies, ~4.2M concepts & ~7.9M termsUses NCIBI Mgrep, a syntactic concept recognizer High degree of accuracy Fast, scalable, Domain independent 13 INRIA - EXMO seminar - March 24th, 2010`

67. Uses mapping in UMLS Metathesaurus and NCBO BioPortal

68. Uses semantic- similarity algorithms based on the is_a graph (ongoing work)

69. Components available as web services14 INRIA - EXMO seminar - March 24th, 2010`

71. NCI/C0025201, Melanocyte in NCI Thesaurus

72. 39228/DOID:1909, Melanoma in Human Disease

73. Is_a closure expansion

74. 39228/DOID:191, Melanocytic neoplasm, direct parent of Melanoma in Human Disease

75. 39228/DOID:0000818, cell proliferation disease, grand parent of Melanoma in Human Disease

76. Mapping expansion

78. The annotation workflow available as a service

79. Automatically process a piece of raw text to annotate it with relevant ontology concepts and return the annotations to the user

80. NCBO Biomedical resources index

81. We have used the annotation workflow to annotate some common resources (gene expression data, clinical trials, articles) and index then by concepts

82. Semantic expansion components

83. http://bioportal.bioontology.org/INRIA - EXMO seminar - March 24th, 2010 16

85. Ontology-based service

86. Semantically described results

87. OWL ontology that formalizes the service model

88. Higher expressivity (e.g., SWRL rules)

89. Annotations returned to the users in OWL as instances

90. Software agents and semantic web technology stack (e.g., SPARQL)

91. What distinguish the service?

92. Can be integrated in current workflow (service-oriented approach)

93. Uses semantic expansion

94. Creates annotations for both all UMLS and NCBO ontologiesINRIA - EXMO seminar - March 24th, 2010 17 [AMIA STB 09] [BMC BioInformatics 09]

95. NCBO Annotator in BioPortal INRIA - EXMO seminar - March 24th, 2010 18

97. The index can be used to enhance search & data integrationINRIA - EXMO seminar - March 24th, 2010 19 [DILS 08] [BMC BioInfo09] [IC 10]

98. Ex: annotation of a GEO element 20

99. Ex: search of a GEO element 21

100. OBR results available in NCBO BioPortal INRIA - EXMO seminar - March 24th, 2010 22 Example of resource available (name and description) Number of annotations in the OBR index Ontology concept/term browsed Title and URL link to the original element Context in which an element has been annotated ID of an element

102. 24 Good use of the semantics (2/2) INRIA - EXMO seminar - March 24th, 2010

104. Higher precision & faster

105. Not limited to UMLS terminologies

106. 22 resources the annotation index

107. Between 99% and 100% of the processed elements are annotated

108. The number of annotating concepts ranges from 359 to 769

109. Functional comparative evaluation with online queries

110. Superior average number of results (i.e., resource elements)

111. Higher number of terms for which we return results. Significant improve in the case of AE or GEO.INRIA - EXMO seminar - March 24th, 2010 25 [BMC BioInformatics 09]

112. Technical details Workflow and pre-computation of the data Java, JDBC & MySQL (prototypes) Java, Spring/Hibernate & MySQL (production) Services deployed as REST web services Tomcat & RestLet INRIA - EXMO seminar - March 24th, 2010 26

113. 27 Users… INRIA - EXMO seminar - March 24th, 2010 Ontology-based services (OBS) NCBO Biomedical Resources index service NCBO Annotator web service BioPortal services UMLS services UCSF Laboratree CollabRx UCHSC PharmGKB, JAX HGMD BioPortal UI PDB/PLoS I2B2 NextBio IO informatics “Resources” tab` Knewco IO informatics CaNanoLab

115. Decide which clinical trials are relevant for a particular patient. Use the annotator service to map clinical-trial eligibility criteria to concepts from UMLS

116. For accelerating curation

117. Use concepts recognized in the abstracts of publications to triage papers for curation

118. For structuring Web data

119. Ensure that any textual annotation created in Laboratree also has corresponding ontology-based annotations

120. For mining gene expression data

121. http://gminer.mcw.edu/INRIA - EXMO seminar - March 24th, 2010 28

123. Semantic annotations can bridge the gap between resources and ontologies

124. Ontology-based annotations are essential for new semantic services on the Web

125. Our annotation workflow offers high-throughput, semantic annotation

126. Offers an automated, service-oriented vision

127. Leverages the knowledge contained in ontologies

128. Please try it and join us!INRIA - EXMO seminar - March 24th, 2010 29

130. NLP and text mining techniques

131. to enhance the recognition of concepts

132. to avoid noise

133. Recognizing relations

134. Enhanced semantic expansion

135. Extracting a similarity measure from the annotation index

136. Composing semantic expansion components on demand

137. Methods for annotation evolution

138. Allowing users to update the annotations previously done by the service

139. Updating the annotations in the index as metadata change

140. Parameterization of the scoring algorithm on demandINRIA - EXMO seminar - March 24th, 2010 30

142. Dr. Nigam H. Shah M.D., PhD initiator of the project & biomedical expert

143. Pr. Mark A. Musen NCBO PI

144. Dr. Adrien Coulet postdoc, new resources within resource index & extraction

145. Cherie Youn Software developer & production releases

146. Nipun Bhatia CS student Mgrep evaluation

147. @ Univ. of Victoria

148. Chris Callendar Software developer (UI)

149. @ NCIBI, Univ. of Michigan

150. Manhong Dai Mgrep designers & developers

151. Dr. Fan Meng 31 INRIA - EXMO seminar - March 24th, 2010

152. Thank youNational Center for BioMedical Ontologyhttp://www.bioontology.orgBioPortal, biomedical ontology repositoryhttp://bioportal.bioontology.orgContact mejonquet@stanford.edu