The document discusses the development of a semantic framework, called icyrus, for efficiently discovering and integrating biomedical images, addressing current challenges related to data management and interoperability in biomedical research. It emphasizes the importance of semantic APIs for enhancing access to biomedical images and proposes solutions to improve knowledge extraction and reuse through linked data services. The framework aims to support clinicians, researchers, educators, and patients in their search for relevant biomedical imagery and information.

1. A Semantic Framework for Biomedical Image
Discovery
Ahmad C. Bukhari , Mate Levente Nagy, Michael Krauthammer , Paolo Ciccarese ,
Christopher J. O. Baker
1

2. Background and Motivation
• Rapid development in Biomedical research produces a
continuous stream of new knowledge.
• Efficient literature accessing practices are essential to transfer of
information from the research community to peer investigators
and other healthcare practitioners.
• Images depict key findings of research papers and help
academically to better understand the biological concepts.

3. Background and Motivation
Protein and DNA sequence Images: Provide exact specification of the
composition of a biological entity.
Pathway diagrams:
Signaling flow / interacting proteins.

4. Background and Motivation
MRI Scans - locations of specific brain
activity.
Gel Imaging - information about DNA and
protein manipulation
Ultrasoun
d
X-
Rays
Graphs

5. Background and Motivation
• Making biomedical image content explicit is essential with regards to making
medical decisions such as:
5
 Diagnosis, Treatment and Follow-up
 Data management and the secondary use for biomedical research
 Assessment of care delivery.
• However, the issues associated with knowledge management and
utility operations unique to image data are only recently gaining
recognition.

6. Background and Motivation
6
In our previous work, we have developed

7. Yale Image Finder
• Yale Image Finder (YIF) is one of the most widely accessed biomedical image
search engines.
• It retrieves biomedical images and associated data based on queries made
over the metadata of the images.
• YIF also searches within the image using a sophisticated image segmentation
method followed by OCR
• YIF repository currently holds over two million biomedical images and
associated metadata in its index.
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8. Challenges
• Searching for images of a certain type is error prone as images are still opaque to
information retrieval and knowledge extraction engines.
• In the Life Sciences, spreadsheets, databases and XML files continue to be the
conventional formats used to store experimental data, e.g. Biota , DrugBank and Open
Microscopy Environment (OME)
• The fact that data exists only in these legacy formats frequently impedes data integration
and significantly impedes scientific knowledge discovery.
 Interoperability and Reusability
 Data integration
 Image Provenance (Orphan Data)
 Semantic Search is not possible

9. Proposed Solution
• To overcome these issues and to accelerate the adoption of the YIF for next
generation biomedical applications, we have developed a publically
accessible semantic API for biomedical images with multiple modalities called
• iCyrus is powered by a dedicated semantic architecture that exposes the YIF
content as linked Image data
9

10. Proposed Solution
• iCyrus permits integration with related information resources and consume by
linked data-aware data services.
• To facilitate the adhoc integration of image data with other online data
resources, we also built semantic web services for iCyrus, such that it is
compatible with the SADI framework.
• We have extended iCyrus functionalities further through the incorporation of
Domeo.
• The iCyrus triplestore currently holds more than thirty-five million triples and
can be accessed and operated through syntactic or semantic query interfaces.
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11. iCyrus Process diagram
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12. Stage 1
• At stage 1, Image datasets are acquired from Yale
Image Finder repository to build a knowledgebase for
iCyrus API
• Establishes connections with YIF and PubMed
concurrently to crosscheck the image metadata
• Resolve Image redundancy and completes the
metadata information
• Stored the clean image data in mysql as parallel
storage with triplestore
12

13. Stage 2
• The foremost task in semantic data publication is defining
appropriate semantic vocabularies.
• Reusability is considered a noble practice in semantic web
application development and it is generally accepted that
well-known semantic vocabularies
• To identify appropriate semantic mappings between
available ontologies and the YIF metadata, we created a
Java program that suggests possible mappings.
13

14. Stage 2 (Continue)
A cursory evaluation of the derived mappings showed there were
three types of results;
① Mappings that fully met our requirements which suggested
predicates such as hasPubMedID and hasPMCID in the FRBR-
aligned Bibliographic Ontology (fbio)
② Mappings that were insufficiently defined, like the imageFeature
property that exists in DICOM Ontology
③ Mappings with hosted resources that did not appear trustworthy.
To manage the new vocabularies that fulfill the requirements
of iCyrus and SEBI
14

15. BIM Ontology
◼ Biomedical Image Ontology provides the semantic vocabularies to all modules of
SEBI.
◼ BIM vocabularies can be categorised into four types:
 Annotation vocabularies hasImageAnnotationSet, hasSequenceType
 Provenance Vocabularies hasAnnVerfiBy, hasCreatedBy
 Features and function vocabularies hasSequenceMotif, hasConservedResidue
 Semantic service vocabularies SADI services input and output
◼ It maintains the provenance on semantic annotations.

16. BIM Model of automatic sequence annotation by a web

17. 17
BIMcrowd-sourceModelingofabiomedical
Image

18. 18
BIMModelingforImageassociatedtext

19. UNB-VPS (Semantic Vocabularies Publishing Server)
http://cbakerlab.unbsj.ca/unbvps
 UNB-VPS is vocabulary publishing server deployed at UNB SJ campus and are used to publish the
semantic vocabularies.

20. The BIM Ontology

21. Stage 2 (Continue)
• we developed a customizable domain-dependent schema mapper
for the curation of initial mappings based on our MySQL stored
data.
• With the help of the Jena Model API and domain-dependent
schema mapper, we RDFized the image data.
• At last stage, we stored the RDFized data into Sesame Triple
store
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22. Stage 3
• To expose the linked open data, we configured a
Sesame triple store and deployed SNORQL, an
AJAX front-end for exploring RDF SPARQL
endpoints
• SNORQL permits users to view and export data in
XML, XHTML and JSON - a javascript object
notation, a popular format among web developers.
• To facilitate end user navigation for technical users
through iCyrus linked data, we deployed Pubby , a
Linked Data interface for local and remote SPARQL 22

23. 23
iCyrus SNORQL endpoint

24. 24
iCyrus Linked data explorer

25. Stage 4
• To demonstrate iCyrus’ usability as a semantic image
API in general, we developed a number of web services
using the SADI framework to advertise their availability.
• Web services are effective medium for the use of
software functionalities in distributed environments
without deploying the entire application on the client
machine.
• A plethora of Bioinformatics software is available on the
internet, but most of them have their own accessing
criteria and information exchange formats. 25

26. Stage 4 (Continue)
• To get full benefit out of these utilities, output should be available in an integrated and
 Available web services protocols - REST, SOAP WSDL etc.
 Non Semantic
 Schema oriented therefore interoperability and scalability issues

27. SADI Framework- Stage 4 (Continue)
• The SADI framework is a set of conventions for creating HTTP-based semantic
web services that can be automatically discovered and orchestrated
• SADI services consume RDF document(s) as input and produce RDF
document(s) as output
 It solves the interoperability problem.
• SADI framework is designed to achieve semantic interoperability among web
services designed for different purposes.

28. Use Case (getAlternateGeneName)
SADI Service Modeling

29. iCyrus with Federated Query Client
• We configured iCyrus’ SADI services registry with the
SHARE federated query engine to illustrate SHARE’s
automatic information discovery feature.
• As an example, we ran the following query: Display
images of the ‘IDS gene’ from all documents along with
their captions and extend the search with alternate
gene names.

30. iCyrus Plugin for DOMEO
• The Domeo collection of software components provides a rich set of
features that can be further extended through the development of new
software plugins.
• In order to provide the additional information about the scientific contents in
Domeo, we mashed up metadata provided by Yale Image Finder through
the iCyrus SPARQL endpoint.
• We developed a server-side software connector that, given a PubMed
Central document, is able to query the iCyrus SPARQL end-point for all the
metadata related to a document’s images.

31. 31
Domeo Environment
iCyrus Images is crowd
Annotated and annotati
Are stored back to triple

32. SEBI as an Extension of iCyrus
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• Image-first knowledge discovery framework to foster the efficient biomedical
literature searching practices and to facilitate the biological image discovery
and reuse.
Working
• SEBI unlocks information associated with and contained in biomedical
sequence images.
• Utilize the information extracted from images to harvest new image
annotations from heterogeneous online biomedical resources. e.g. BLAST,
HMMER
• SEBI incorporates knowledge infrastructure components and services
including image feature extraction, Semantic Web data services, linked openSEBI stands for Semantic Enrichment of Biomedical Images

33. Semantic Sequence Image Enrichment
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34. Related Image Finding in SEBI
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 SEBI employs the cosine similarity along with fuzzy rule engine to discover and categorize the rel

35. Who will get benefit out of this work?
35
Clinician looks for the visual representation of a disease or condition
Researcher searches for studies with certain types of analyses
Students seek for diagrams that elucidate complex processes such as DNA
replication
Professional or educator look for an image for a presentation
Patient wants to better understand his disease.

36. Any Question?
Scan for project page
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