Mapping a human brain generates petabytes of gene listings and the corresponding locations of these genes throughout the human brain.  Due to the large dataset a prototype Semantic Web application was created with the unique ability to link new datasets from similar fields of research, and present these new models to an online community.  The resulting application presents a large set of  gene to location mappings and provides new information about diseases, drugs, and side effects in relation to the genes and areas of the human brain. In this presentation we will discuss the normalization processes and tools for adding new datasets, the user experience throughout the publishing process, the underlying technologies behind the application, and demonstrate the preliminary use cases of the project.

1. APPLIED LINKED DATA
AND SEMANTIC
TECHNOLOGY
Expanding a Neurobiology Dataset

2. Today we are discussing…
• What is the use case and who requested it?
• How do you import and normalize thousands of RDF
•
•
•
•
triples worth of gene data?
How do we enrich the normalized gene data with parallel
research data sets?
Creating instance pages without knowing exactly what will
be displayed on them.
Demonstration of the initial use cases
Question and answer session

3. Why?
• Prototype: How do we assemble the data mine and
refine the authoring tools?
How do we expand this to the research
community?
• How do we expand ownership of the data to research
professionals?
• How do we build systems in a way that research
professionals can author and link the data?
• How do we publish these new relationships to the wider
research community?

4. What is the Allen Institute for Brain
Science?
• Launched in 2003 with seed funding from founder and
philanthropist Paul G. Allen.
• Serving the scientific community is at the center of our mission
to accelerate progress toward understanding the brain and
neurological systems.
• The Allen Institute's multidisciplinary staff includes
neuroscientists, molecular biologists, informaticists, and
engineers.
“The Allen Institute for Brain Science is an
independent 501(c)(3) nonprofit medical
research organization dedicated to accelerating
the understanding of how the human brain
works.”

5. Human Brain Map
• Open, public online access
• A detailed, interactive three-
•
•
•
•
dimensional anatomic atlas of the
"normal" human brain
Data from multiple human brains
Genomic analysis of every brain
structure, providing a quantitative
inventory of which genes are
turned on where
High-resolution atlases of key brain
structures, pinpointing where
selected genes are expressed
down to the cellular level
Navigation and analysis tools for
accessing and mining the data

6. Biological Linked Data Map
• Open, public online access
• Data from multiple RDF data
•
•
•
•
stores
Complete import pipeline using
LDIF framework
Outlines of each imported
instance embedding inline wiki
properties and providing views of
imported properties from original
RDF datasets
Charting tools that „pivot‟ SPARQL
queries providing several views of
each query
Navigation and composition tools
for accessing and mining the data

7. Where did we get the data?
• KEGG : Kyoto Encyclopedia of Genes and Genomes
• “KEGG GENES is a collection of gene catalogs for all complete genomes
generated from publicly available resources, mostly NCBI RefSeq.”
• Diseasome
• “The Diseasome website is a disease/disorder relationships explorer and
a sample of an innovative map-oriented scientific work. Built by a team of
researchers and engineers, it uses the Human Disease Network data set.”
• DrugBank
• “The DrugBank database is a unique bioinformatics and cheminformatics
resource that combines detailed drug data with comprehensive drug target
information.”
• SIDER
• “SIDER contains information on marketed medicines and their recorded
adverse drug reactions. The information is extracted from public
documents and package inserts.”

8. New ontology map for import
•
Genes
•
•
•
•
Diseases
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•
•
DrugBank : 4,772
KEGG : 2,482
SIDER : 924
Effects
•
•
Diseasome : 4,213
KEGG : 459
Drugs
•
•
•
•
DrugBank : 4,553
Diseasome : 3,919
KEGG : 9,841
SIDER : 1,737
Pathways
•
KEGG : 28,442
We chose to intentionally simplify the
ontology due to disagreements between
researchers about entity relationships and
subclasses.

9. Importing and mapping the Linked Data
•
R2R
•
•
•
•
32,900 instances were converted to the
wiki ontology.
Networked
Storage
Local
Storage
Download
583,746 properties mapped
Pathways were ignored for wiki
ontology import, but are available within
the triple store KEGG Pathway graph.
SIEVE
•
20,849 instances available in wiki
ontology after SILK normalization
•
Instance merging effected drugs,
genes, and diseases across datasets.
• Triple Store SPARQL Update
R2R
Mapping
Engine
Maps Entities to
New Ontology
Import to
Wiki
Sieve
Mapping
Engine
Normalizes Entities
across data sources
Normalize
Entities
Triple
Store
Available with
SPARQL Queries

10. Importing and mapping the Linked Data
•
R2R
•
•
•
•
32,900 instances were converted to the
wiki ontology.
Networked
Storage
Local
Storage
Download
583,746 properties mapped
Pathways were ignored for wiki
ontology import, but are available within
the triple store KEGG Pathway graph.
SIEVE
•
20,849 instances available in wiki
ontology after SILK normalization
•
Instance merging effected drugs,
genes, and diseases across datasets.
• Triple Store SPARQL Update
R2R
Mapping
Engine
Maps Entities to
New Ontology
Import to
Wiki
Sieve
Mapping
Engine
Normalizes Entities
across data sources
Normalize
Entities
Triple
Store
Available with
SPARQL Queries

11. LDIF: LINKED DATA
INTEGRATION
FRAMEWORK
Expanding a Neurobiology Dataset

12. Linked Data challenges
• Data sources that overlap in content may:
• Use a wide range of different RDF vocabularies
• Use different identifiers for the same real-world entity
• Provide conflicting values for the same properties
• Implications
• Queries become hand crafted for a specific RDF data set – no
different than using a proprietary API.
• Individual, improvised and manual merging techniques for data
sets.
• Integrating public datasets with internal databases poses
the same problems

13. Linked Data Integration Framework
• LDIF normalizes the Linked Data from multiple sources
into a clean, local target representation while keeping
track of data provenance.
1
Collect data: Managed download and update
2
Translate data into a single, target vocabulary
3
Resolve identifier aliases into local target URIs
4
Cleanse data and resolve conflicting values
5
Output to local file system or triple store

14. LDIF Pipeline
1
Collect data
2
Translate data
3
Supported Data Formats
Resolve
identities
4
Cleanse data
5
Output data
•
•
•
RDF Files (Multiple Formats
SPARQL Endpoints
Crawling Linked Data
Component Stack

15. LDIF Pipeline
1
Collect data
2
Translate data
Sources use a wide range of different
RDF vocabularies
dbpedia-owl:City
schema:Place
R2R
location:City
fb:location.citytown
3
Resolve
identities
4
Cleanse data
5
Output data
Component Stack

16. LDIF Pipeline
1
Collect data
2
Sources use different identifiers for the
same entity
Translate data
London, England
London, MA, USA
London, TN, USA
London, TX, USA
SILK
London
3
London =
London, England
Resolve
identities
4
Cleanse data
5
Output data
Component Stack

17. LDIF Pipeline
1
Collect data
2
Translate data
3
Sources provide different values for the
same property
London, England
has a population
of 8.174M people
London, England
has a population
of 9.2M people
SILK
rdfs:population:
8.174M
Resolve
identities
4
Cleanse data
5
Output data
Component Stack

18. LDIF Pipeline
1
Collect data
2
Translate data
3
Supported Output Formats
•
•
•
N-Quads
N-Triples
SPARQL Update Stream
Resolve
identities
4
Cleanse data
5
Output data
Provenance tracking using Named Graphs
Component Stack

19. LDIF Architecture

20. Normalized Linked Data is not always
pretty.

21. Normalized Linked Data is not always
pretty.

22. SELECT DISTINCT ?group1 ?item1 ?group2 ?item2 {
GRAPH ?G {
?target drugbank:geneName "{{{1}}}" ;
drugbank:geneName ?geneName ;
.
?drug drugbank:target ?target ;
drugbank:genericName ?item2 ;
drugbank:affectedOrganism ?group2 ;
.
}
GRAPH ?G1 {
?siderDrug sider:drugName ?item2 ;
rdfs:label ?group1 ;
sider:sideEffect ?effect;
.
?effect rdfs:label ?item1 .
}
}

23. Semantic MediaWiki
Semantic MediaWiki is a full-fledged framework, in
conjunction with many spinoff extensions, that can turn a
wiki into a powerful and flexible knowledge management
system. All data created within SMW can easily be
published via the Semantic Web, allowing other systems to
use this data seamlessly.

24. Four initial templates for each instance by
category
1. Custom infobox within outline
template
•
Visible inline properties
2. Outline template providing instance
information
3. Widget template displaying dynamic
charts or third party services
•
Donut charts and AIBS gene feed
4. Broad table SPARQL queries
showing instance relationships
5. Hidden inline properties for other
extensions

25. Creating instance wiki pages
• The Triple Store now contained tens of
thousands of recognized category
instances. Creating the pages require a
bot.
Create List of Page
Names
1.0
RDF Data
Download
1. Fetch the RDF dumps from an active
D2R server
2. Use regex to fetch the rdf:label property
that was mapped by R2R as an instance
name
3. Open category specific text file of wiki
markup (page of template includes)
4. Contact Neurowiki and request a new
page from the list of names with the
category content
Sanitize
Script
2.0
Create CSV
Category
Page Names
Text of Wiki
markup for page
instance
Read Open
3.0
Create MediaWiki Page
MediaWiki
Gateway rb
Framework
REST
interface
4.0
Neurowiki
Instance
Page

26. Final application stack
JavaScript View Layer (High Charts / Sproutcore / JQuery)
Semantic MediaWiki
Triple Store
(Virtuoso)
Relational Database
(MySQL)
LDIF
AIBS REST API
(Gene Heat Map Data)
AIBS
Diseasome
DrugBank
SIDER
KEGG

27. NEUROWIKI
Expanding a Neurobiology Dataset

28. How are base entities like Calcium
represented?
1. The wiki page and
corresponding template
components are rendered.
Drug Search
1.0
Wiki Page
Aggregate
Page of
Components
2. Relations are pulled from the
normalized data store of linked
data.
2.0
Calcium
Relations
Neurobase
Data Stores
3. The JavaScript components are
3.0
Selected
Widget for
Display
populated via a data feed

29. How are base entities like Calcium
represented?
• Because so many
organisms contain
calcium the
mappings to
affected species
were never created
to conserve space
in the data store.
Drug and Disease Class Ratios of Calcium
Inner Circle: Drugs by Affected Species, Outer Circle: Disease Ratios by Class

30. What are the dangers of Propofol?
1. Propofol DrugBank relations are
Drug Search
Neurobase
Data Stores
rendered in corresponding
JavaScript components.
1.0
Propofol
Relations
2.0
Aggregate
Components
2. The Diseasome disease
relations show classes of illness
Propofol affects.
Propofol
Disease
Relations
3. An aggregate of SIDER side
3.0
Propofol Side
Effects
effects are rendered in relation
to Propofol and disease classes.

31. What are the dangers of Propofol?

32. What are the dangers of Propofol?

33. What are the dangers of Propofol?

34. Which drugs are used in Chemotherapy?
1.
2.
Disease
Search
DrugBank and AIBS relations to
genes affected by both the disease
and drug.
3.
SIDER side effects related to the
gene, disease, and drug.
4.
DrugBank drug glossary definition
specifying various forms of Cancer
treatment.
Neurobase
Data Stores
1.0
Disease
Relations
Diseasome disease relations
normalized by LDIF.
Aggregate
Components
2.0
Gene Drug
Relations
3.0
Drug Side
Effects
4.0
Drug Info Box

35. Which Drugs are used in Chemotherapy?

36. Which drugs are used in Chemotherapy?
Drug and Disease Class Ratios of AR
Inner Circle: Drugs by Affected Species, Outer Circle: Disease Ratios by Class

37. Which drugs are used in Chemotherapy?
Drug and Side Effect Ratios of AR
Inner Circle: Drugs by Affected Species, Outer Circle: Side Effect Ratios of Drugs

38. Which drugs are used in Chemotherapy?

39. Which drugs are used in Chemotherapy?
Drug and Disease Class Ratios of Nilutamide
Inner Circle: Drugs by Affected Species, Outer Circle: Disease Ratios by Class

40. Which drugs are used in Chemotherapy?
Drug and Disease Class Ratios of Bicalutamide
Inner Circle: Drugs by Affected Species, Outer Circle: Disease Ratios by Class

41. Which drugs are used in Chemotherapy?

42. Expanding the Prototype
• Semantic MediaWiki query construction
• Could this be done in SPARQL?
• Authoring SILK / R2R mappings for the LDIF Pipeline
• Extremely difficult and the editors are not intuitive
• How do you get data owners to fuse the sets and create
the data store themselves?
• Tested with Aura Wiki prototype
• Expand authoring provenance
• How do we ensure new data / links comes from an authoritative
source?

43. Today we discussed…
• The Allen Institute for Brain Science (AIBS)
• Four similar research data sets to interlink with the AIBS
•
•
•
•
•
data set
An import pipeline named Link Data Integration
Framework (LDIF)
The interlinking process for 5 concurrent research data
sets (AIBS, DrugBank, Diseasome, KEGG, SIDER)
A prototype neurobiology authoring platform.
Creating instance pages to display the new connections.
Demonstration of the initial use cases.

44. QUESTIONS?
COMMENTS?
Expanding a Neurobiology Dataset

45. THANK YOU.
Expanding a Neurobiology Dataset