Some of the recent work we've been doing with SADI and SHARE, using SHARE as a mechanism for dynamically converting OWL Classes into workflows in a data-dependent manner; OWL, in this case, is acting as an abstract workflow model. The slides in the middle are the usual SADI/SHARE explanation; the slides at the end show how we're using these dynamically generated workflows to "personalize" medical information on the Web for a particular patient's profile.

1. Carole Goble
“Shopping for data
should be as easy
as shopping for
shoes!!”

2. Evaluating Hypotheses
using SPARQL-DL as an
abstract workflow language
to choreograph
SADI Semantic Web Services
Mark Wilkinson, Isaac Peral Senior Researcher in Biological Informatics
Centro de Biotecnología y Genómica de Plantas, UPM.

3. Start at the end...

4. We wanted to duplicate
a real, peer-reviewed, bioinformatics analysis
simply by providing a model describing
what the answer
(if one exists)
would look like...

5. ...the machine had to make
every other decision
on it’s own

6. This is the study we chose:
Gordon, P.M.K., Soliman, M.A., Bose, P.,
Trinh, Q., Sensen, C.W., Riabowol, K.
Interspecies data mining to predict novel
ING-protein interactions in human.
BMC Genomics 9, 426 (2008).

7. Gordon, P.M.K., Soliman, M.A., Bose, P., Trinh, Q., Sensen, C.W., Riabowol, K.: Interspecies
data mining to predict novel ING-protein interactions in human. BMC genomics. 9, 426 (2008).

8. Original Study - simplified and abstracted:
Using what is known about interactions in other species,
predict new interactions in your species of interest
Given a protein P in Species X
Find proteins similar to P in Species Y
Retrieve interactors in Species Y
Sequence-compare Y-interactors with Species X genome
(1)  Keep only those with homologue in X
Find proteins similar to P in Species Z
Retrieve interactors in Species Z
Sequence-compare Z-interactors with (1)
 Putative interactors in Species X

9. For our prototype study, we simplified this further to:
X 2
Then intersect

10. The tricky part is...
In the abstract, the two workflows
are identical
but in reality they will be different
because they call for information
from different species

11. Modeling the result – Step 1
OWL
Web Ontology Language (OWL) is the
language approved by the W3C
for representing knowledge on the Web

12. Modeling the result – Step 2
ProbableInteractor:
is homologous to (
protein from ModelOrganism1…) # Potential Interactor in previous slide
and
protein from ModelOrganism2…) # Potential Interactor in previous slide
Probable Interactor is defined in OWL as a subclass of Potential Interactor
that requires homologous pairs of interacting proteins to exist in both
comparator model organisms.
(Effectively, an intersection)

13. We then publish our OWL model of a Probable Interactor on the Web
In a local data-file we provide the protein we are interested in,
and the two species we wish to use in our comparison
taxon:9606 a i:OrganismOfInterest . # human
uniprot:Q9UK53 a i:ProteinOfInterest . # ING1
taxon:4932 a i:ModelOrganism1 . # yeast
taxon:7227 a i:ModelOrganism2 . # fly
These four lines represent all of the data provided to the
query I am about to show you...

14. This is the question we ask:
PREFIX i: <http://sadiframework.org/ontologies/InteractingProteins.owl#>
SELECT ?protein
FROM <file:/local/workflow.input.n3>
WHERE {
?protein a i:ProbableInteractor .
}
The reference to our OWL model of the answer

15. Our system then derives (and executes) the following workflow automatically
These are different
Web services!
...selected at run-time
based on the same model

16. There are two very cool things about what you just saw...

17. There are two very cool things about what you just saw...
The system was able to
create a workflow based on
an OWL model

18. There are two very cool things about what you just saw...
The workflow it created
(i.e. the services chosen)
differed depending on
context

19. We got the answer
“simply” by designing a model of the answer!

20. Start at the beginning...

21. Semantic Automated Discovery and Integration
A Semantic Web-focused Web Services specification
Microsoft
Research http://sadiframework.org

22. Web Services
vs.
Semantic Web

23. Web Services
XML + XML Schema
Semantic Web
RDF + OWL

24. Web Services
POST of SOAP
Semantic Web
GET of RDF

25. Web Services
No (rigorous) semantics
Semantic Web
Rich, flexible semantics

26. Web Services
&
Semantic Web
Fundamentally different
Web technologies

27. A design-practice for
Web Service provision on the
Semantic Web

28. 100% standards-compliant
with no “invented” standards

29. Lightweight
(only 2 inter-related “rules”)

30. Rules come from
observations:

31. SADI Observation #1:
Web Services in Bioinformatics create
implicit biological relationships
between their input and output

32. SADI Observation #1:
SeqRet

33. SADI Design Practice #1
Make the implicit explicit…
A Web Service should create “triples” linking
the input data to the output data thus
explicitly describing the semantic relationship
between them

34. SADI Best Practice #1
This is what bioinformatics Web Services
implicitly do anyway!
Easy to implement this as a
best-practice
...and makes SADI Services a source of Linked Data

35. SADI Observation #2:
HTTP GET and POST
GET guarantees
the response relates to the request URI
in a very precise and predictable way
POST does not…

36. SADI Observation #2:
HTTP GET and POST
That’s why Web Services have a fundamentally
different behaviour than the Semantic Web

37. SADI Observation #2:
GET and POST
We can fix that!
(without breaking any existing rules or standards!)

38. SADI Best Practice #2
SUBJECT URI of the output graph (triples)
is the same as
SUBJECT URI of the input graph (triples)
(the output is “about” the input... Now explicitly!)

39. SADI Best Practice #2
GeneID GeneID
rdf:type rdf:type
BRCA1 BRCA1
hasDNASequence
SeqRet AGCTTA...

40. Consequence
Web Services now exhibit a very similar
behavior to the Web itself
POST “behaves like” GET

41. SADI Interfaces
Service Interfaces can be described by
two OWL classes
(this is 100% compatible with the SAWSDL standard)

42. SADI Interfaces
OWL Class #1: My Input Class

43. SADI Interfaces
OWL Class #2: My Output Class

44. SADI Service Functionality
Consumes OWL Individuals of Class #1
Returns OWL Individuals of Class #2
but the URI of those two GeneID GeneID
individuals is the same; they are rdf:type
rdf:type
BRCA1 BRCA1
the same individual, just now a hasDNASequence
member of a new class. SeqRet AGCTTA...

45. In practice, of course, you don’t return the input data
Strip it and add the new data provided by the Service
But since the output is still “rooted” in the input node,
Input and output are easily merged client-side
(just concatenate the output with the input)

46. Service Description
INPUT OWL Class
NamedIndividual: things with
a “name” property
from “foaf” ontology
OUTPUT OWL Class
GreetedIndividual: things with
a “greeting” property
from “hello” ontology
POST http://example.org/myservice
person:1
foaf:name rdf:type
hello:Named
Guy Incognito Individual
person:1
hello:greeting rdf:type
hello:Greeted
Hello, Guy Incognito! Individual

47. How do we discover services?
Input and output are about the same “thing”
Therefore, to describe what a service does
simply compare (“diff”) the
Input and Output OWL classes
This is not prescriptive! Just how we use it

48. Service Description
INPUT OWL Class
NamedIndividual: things with
a “name” property
from “foaf” ontology
OUTPUT OWL Class
GreetedIndividual: things with
a “greeting” property
from “hello” ontology
The service provides
POST http://example.org/myservice
a “greeting” to a
Named Individual
person:1
based on its “name”
foaf:name rdf:type
hello:Named
Guy Incognito Individual
person:1
hello:greeting rdf:type
hello:Greeted
Hello, Guy Incognito! Individual

49. Service Discovery
Index all of the properties
added by all of the services
under all circumstances

50. Real-world Example
Input Data: BRCA1 rdf:type Gene ID
Output Data: BRCA1 hasDNASequence AGCTTAGCCA…
Registry Index: Service provides “hasDNASequence” property to Gene IDs

51. Service Discovery
Simply search for the property of interest
based on the data in-hand

52. e.g. The question:
“what is the DNA sequence of BRCA1?”
Discover a SADI Web Service that generates the
DNA Sequence property for gene identifiers

53. DEMO
Knowledge Explorer
Plug-in
For more information about the Knowledge Explorer surf to:
http://io-informatics.com

55. SADI has just filled-in “Encodes” property for the three genes
from the output of discovered Web Service(s)

57. Discover services that provide the hasGOTerm
property for Protein Sequence datatype

59. This kind of “Web Service Surfing” is very
intuitive for the Biologist!
No need to describe the algorithm or the database
just describe the properties that will be added

60. Semantic Health And Research Environment
SHARE answers arbitrary SPARQL queries
by finding and executing SADI Services

61. Example #1
What is the phenotype of every allele of the
Antirrhinum majus DEFICIENS gene
SELECT ?allele ?image ?desc
WHERE {
locus:DEF genetics:hasVariant ?allele .
?allele info:visualizedByImage ?image .
?image info:hasDescription ?desc
}

62. Example #1
What is the phenotype of every allele of the
Antirrhinum majus DEFICIENS gene
SELECT ?allele ?image ?desc
WHERE {
locus:DEF genetics:hasVariant ?allele .
?allele info:visualizedByImage ?image .
?image info:hasDescription ?desc
}
Note that there is no “FROM” clause!
We don’t tell it where it should get the information,
The machine has to figure that out by itself...

63. Enter that query into
SHARE

64. Click “Submit”...

65. ...and in a few seconds you get your answer.
Based on predicates in your query, SHARE utilized SADI
to automatically discover the resources required to answer your question.

66. Because it is the Semantic Web
The query results are live hyperlinks
to the respective Database or images

67. Importantly
We posed, and answered a
complex database query
WITHOUT A DATABASE

68. (in fact, the data didn’t even have to exist... as I’ll now show you)

69. Example #2
Show me the latest Blood Urea Nitrogen and Creatinine levels
of patients who appear to be rejecting their transplants
SELECT ?patient ?bun ?creat
FROM <http://sadiframework.org/ontologies/patients.rdf>
WHERE {
?patient rdf:type patient:LikelyRejecter .
?patient l:latestBUN ?bun .
?patient l:latestCreatinine ?creat .
}

70. Likely Rejecter:
A patient who has creatinine levels
that are increasing over time
- - Wilkinson “MD”

71. Likely Rejecter:
Our database contains various
blood chemistry measurements
at various time-points

72. Likely Rejecter:
…but there is no “likely rejecter”
column or table in our database…

73. SHARE determines
by itself
the need to do a
Linear Regression analysis over
Creatinine blood chemistry measurements

74. SHARE determines
by itself
how and where that analysis
can be done
and does it

75. The SHARE system utilizes Semantics (via SADI) to discover and access
analytical services on the Web that do linear regression analysis

76. VOILA!

77. How does SHARE work?

78. Ontologies

79. Ontology Spectrum
Thesauri Frames Selected
“narrower (Properties) Logical
Catalog/ term” Formal Constraints
ID relation is-a (disjointness,
inverse, …)
Terms/ Informal Formal General
Value Logical
glossary is-a instance
Restrs. constraints
Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty;
– updated by McGuinness.
Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

80. Ontology Spectrum
BETTER!!
Thesauri Frames Selected
“narrower (Properties) Logical
Catalog/ term” Formal Constraints
ID relation is-a (disjointness,
inverse, …)
Terms/ Informal Formal General
Value Logical
glossary is-a instance
Restrs. constraints
Basic SADI/SHARE functionality

81. Ontology Spectrum
Because it
fulfils XYZ
Thesauri WHY? Frames Selected
“narrower (Properties) Logical
Catalog/ term” Formal Constraints
ID relation is-a (disjointness,
inverse, …)
Terms/ Informal Formal General
Value Logical
glossary is-a instance
Restrs. constraints
Because
I say so!
Why is this data a member of this OWL Class?
(and therefore valid input to the service)

82. Discovery systems - flexible
Selected
Thesauri Logical
Catalog/ “narrower term” Formal
Frames Constraints
(disjointness,
ID (Properties)
relation is-a inverse, …)
Terms/ Informal Formal Value General
Logical
glossary is-a instance Restrs. constraints
Categorization Systems – like library shelves, inflexible

83. In the upper end of the Ontology Spectrum,
if the data has the right properties
It can be discovered to be a valid input to a Service
regardless of how it was originally classified or which
ontology was used for that classification

84. ...and in the context of a SHARE query
those individual properties may have been aggregated
from many different places;
The data becomes a valid input as properties aggregate

85. In exactly the same way that the OWL property
restrictions of a SADI Input Class tell SHARE what
properties a service requires as input
The property restrictions of an OWL Class in the
SPARQL query tell SHARE what properties it
needs to retrieve to create members of that class

86. Show me the latest Blood Urea Nitrogen and Creatinine levels
of patients who appear to be rejecting their transplants
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#>
PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#>
SELECT ?patient ?bun ?creat
FROM <http://sadiframework.org/ontologies/patients.rdf>
WHERE {
?patient rdf:type patient:LikelyRejecter .
?patient l:latestBUN ?bun .
?patient l:latestCreatinine ?creat .
}

87. The definition of a Likely Rejecter category is encoded in
a machine-readable document written in the OWL Ontology language
Basically:
“the regression line over creatinine measurements should have an increasing slope”

88. SHARE burrows down through the ontological definition
to learn about what the properties of “regression models” are

89. SHARE utilizes SADI to discover analytical services on the Web
that do linear regression analysis then other services that can
determine the “latest” measurements from a time-series

90. Let’s go through that again from a different perspective

91. OWL Classes that include property restrictions can be
“executed” as if they were workflows

92. Ontology = Query = Workflow

94. WORKFLOW

95. QUERY:
SELECT images of
mutations from genes in
organism XXX that
share homology to this
gene in organism YYY

96. Concept:
“Homologous Mutant
Image”

97. As OWL Axioms
HomologousMutantImage
is owl:equivalentTo {
Gene Q hasImage image P
Gene Q hasSequence Sequence Q
Gene R hasSequence Sequence R
Sequence Q similarTo Sequence R
Gene R = “my gene of interest” }

98. Those axioms
combine to
create an
OWL Class:
Homologous
Mutant Image

99. QUERY:
Retrieve
owl:Homologous
Mutant Image for
gene XXX

100. SHARE:
Decomposes the
owl:Homologous Mutant Image
Class, discovers SADI services
relevant to that class, and
pipelines them together into a
workflow

103. “The user experiments showed that
workflow re-use… is difficult for bioinformaticians.”
- Gooderis, A. (2008) Ph.D. Thesis

104. Under slightly different circumstances
(e.g. studying the same phenomenon in a
different organism)
this workflow
WILL NOT SOLVE THE PROBLEM
It must be edited on a case-by-case basis
This editing turns out to be extremely
difficult, and can ~only be done manually

105. This great idea would be even better
if workflows were a little bit easier to re-purpose!

106. With SHARE, a workflow is generated
dynamically, based on all of the information
presented in the query
e.g. to create a new workflow simply specify
a different organism ID in the query!

107. With SHARE, a workflow is generated
dynamically, based on all of the information
presented in the query
Moreover, the workflow plan CHANGES
dynamically based on service outputs!

108. With SHARE, the ontology is the workflow
(not the same as “an ontology that describes workflows”)
The ontology acts as an abstraction of a
workflow, which is concretized at run-
time based on circumstances of the query

109. Works (best) with ontologies in the “Frames+” part of
the spectrum, if there are SADI services available
Selected
Thesauri Logical
“narrower Constraints
Formal Frames
Catalog/ term” (disjointness,
is-a (Properties) inverse, …)
ID relation
Terms/ Informal Formal Value General
Logical
glossary is-a instance Restrs.
constraints

110. As far as we are aware, SHARE is the only system that
exhibits this particular behaviour
...and IMO this is a pretty big deal...

111. Gordon, P.M.K., Soliman, M.A., Bose, P., Trinh, Q., Sensen, C.W., Riabowol, K.: Interspecies
data mining to predict novel ING-protein interactions in human. BMC genomics. 9, 426 (2008).

112. That experiment can now be represented ONCE
as an OWL Class
It becomes concretized automatically
for each individual researcher
as a distinct workflow given any starting protein
and any combination of comparator species

113. This is far beyond simply changing the parameters entered into a workflow...

114. Moreover, the idea of automated workflow “individuality” is quite interesting to us...

115. This is an early prototype of a
Patient-driven Personalized Medicine
Web interface

120. Matching based on official
name, compound name,
brand name, trade name, or
“common name” 

121. Still needs some work...
??!?!?

125. Why the alert?
Link out to PubMed

127. The SADI+SHARE workflow and reasoning was
personalized to YOUR medical data

128. In future iterations, we will enable the workflow
to be further customized through “personalized”
OWL Classes (e.g. Provided by your Clinician!!)

129. These OWL Classes might include information about the
current trajectory of your treatment for a chronic disease,
for example, such that what you read on the Web is
placed in the context of your expert Clinical care...

130. Frankly, I think it’s quite cool that “people”
are creating and running personalized
workflows at the touch of a button...

131. ...as many of you know, that has been my
dream since I started studying this
problem a decade ago!

132. Final thoughts

133. An experiment... based on a hypothesis

134. An experiment... based on a hypothesis
now modeled in OWL
...
...

135. Does this OWL Class represent a Hypothesis?
I think it does!
...
...

136. I believe that we will soon show
using SADI + SHARE
that we can model a non-trivial
hypothetical biological scenario
then evaluate if that hypothesis is supported or not
based on whether the automatically-synthesized workflow
returns any individuals
that conform to the model.

137. Ontology = Hypothesis = Query = Workflow [= Materials and Methods ]
Most of your publication is done!
All you need to do now is interpret the results!
These can be automatically derived through
provenance information during workflow execution

138. Please join us!
SADI and SHARE are Open-Source projects
http://sadiframework.org

139. My New Home!

140. University of British Columbia
Luke McCarthy – Lead Dev. Edward Kawas
Everything... SADI Service auto-generator
Benjamin VanderValk Ian Wood
SHARE & SADI & Experimental modeling & Experimental modeling project
myHeath Button
Soroush Samadian
Cardiovascular data modeling and queries

141. C-BRASS Collaborators at other sites
U of New Brunswick Carleton University
Dr. Chris Baker Dr. Michel Dumontier
Alexandre Riazanov Marc-Alexandre Nolin
Leonid Chepelev
Steve Etlinger
Nichaella Kieth
Jose Cruz

142. Microsoft Research