This is a brief version of earlier talks, but I think it might explain more emphatically what I think Web Science is, and why I believe it is realistic, and how SADI/SHARE technologies (or technologies like them) are important to achieve the vision

1. Using OWL Domain Models as
Abstract Workflow Models
Or...
Conducting in silico research in the Web
from hypothesis to publication
Mark Wilkinson
Isaac Peral Senior Researcher in Biological Informatics
Centro de Biotecnología y Genómica de Plantas, UPM, Madrid, Spain
Adjunct Professor of Medical Genetics, University of British Columbia
Vancouver, BC, Canada.

2. Context
“While it took 2,300 years after the first
report of angina for the condition to be
commonly taught in medical
curricula, modern discoveries are
being disseminated at an increasingly
rapid pace. Focusing on the last 150
years, the trend still appears to be
linear, approaching the axis around
2025.”
The Healthcare Singularity and the Age of Semantic
Medicine, Michael Gillam, et al, The Fourth Paradigm: Data-
Intensive Scientific Discovery Tony Hey (Editor), 2009
Slide adapted with permission from Joanne Luciano, Presentation
at Health Web Science Workshop 2012, Evanston IL, USA
June 22, 2012.

3. “The Singularity”
The X-intercept is where, the moment a discovery is
made, it is immediately put into practice
(not only medical practice, but any research endeavour...)
The Healthcare Singularity and the Age of Semantic Medicine, Michael Gillam, et al, The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey (Editor), 2009
Slide Borrowed with Permission from Joanne Luciano, Presentation at Health Web Science Workshop 2012, Evanston IL, USA
June 22, 2012.

4. The technology required
to achieve this
does not yet exist

5. You
Are
Here
Scientific research would have to be conducted
within a medium that
immediately interpreted and disseminated
the results...

6. You
Are
Here
...in a form that immediately (actively!) affected the
research of others...

7. You
Are
Here
...without requiring them to be aware
of these new discoveries.

8. To achieve this vision
We must learn how to
do research IN the Web
Not OVER the Web

9. How we use
the Web today

10. To achieve this vision
We must learn how to
do research IN the Web
Not OVER the Web

11. I’d like to show you how close
we now are to this vision
and how we got there

12. Web Science 2.0

13. We wanted to duplicate
a real, peer-reviewed, bioinformatics analysis
simply by building a model in the Web
describing what the answer
(if one existed)
would look like

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

15. This is the study we chose:

16. 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).

17. Original Study Simplified
Using what is known about interactions in fly & yeast
predict new interactions with your
human protein of interest

18. Abstracted
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

19. Modeling the answer...
OWL
Web Ontology Language (OWL) is the
language approved by the W3C
for representing knowledge in the Web

20. Modeling the answer...
Note that every word in
this diagram is, in reality, a
URL (because it is OWL)

21. Modeling the answer...
The model of a Potential
Interactor is published in
The Web
It utilizes concepts from
other models published in
The Web
(ours and other’s)
by referencing their URLs

22. Modeling the answer...
The model of a Potential
Interactor is a network of
concepts distributed
within the Web
It will be affected by
changes to those concepts
We do not “own” all of
those concepts!

23. Modeling the answer...
ProbableInteractor
is homologous to (
Potential Interactor from ModelOrganism1…)
and
Potential Interactor from ModelOrganism2…)
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)

24. Publish our OWL model of a Probable Interactor
in the Web

25. Running a Web Science 2.0
Experiment
In a local data-file
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

26. The tricky bit is...
In the abstract, the
search for homology is
“generic” – ANY model
organism.
But when the machine
attempts to do the
experiment, it will have
to use several different
and specific resources
because our question
specifies two different taxon:4932 a i:ModelOrganism1 . # yeast
species taxon:7227 a i:ModelOrganism2 . # fly

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

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

30. There are three very cool things about what you just saw...

31. There are three very cool things about what you just saw...
The system was able to
create a workflow based on
an OWL model (ontology)

32. There are three very cool things about what you just saw...
The system was able to create a
COMPUTATIONAL workflow
based on a BIOLOGICAL model

33. There are three very cool things about what you just saw...
The workflow it created
(i.e. the services chosen)
differed depending on context
taxon:4932 a i:ModelOrganism1 . # yeast
taxon:7227 a i:ModelOrganism2 . # fly

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

35. How did we do that?

36. Design Pattern for
Web Services on the Semantic Web

37. A Web application that answers
SPARQL-DL queries
Query-answering
Enhanced by SADI

38. Demos of SADI and SHARE

39. 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
}

40. 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...

41. Enter that query into
SHARE

42. Click “Submit”...

43. SHARE examines available SADI Web Services
...and in a few seconds you get your answer.

44. The query results are live hyperlinks
to the respective Database or images
(the answer is IN the Web!)

45. What pathways does UniProt protein P47989 belong to?
PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>
PREFIX ont: <http://ontology.dumontierlab.com/>
PREFIX uniprot: <http://lsrn.org/UniProt:>
SELECT ?gene ?pathway
WHERE {
uniprot:P47989 pred:isEncodedBy ?gene .
?gene ont:isParticipantIn ?pathway .
}

46. What pathways does UniProt protein P47989 belong to?
PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>
PREFIX ont: <http://ontology.dumontierlab.com/>
PREFIX uniprot: <http://lsrn.org/UniProt:>
SELECT ?gene ?pathway
WHERE {
uniprot:P47989 pred:isEncodedBy ?gene .
?gene ont:isParticipantIn ?pathway .
}

47. What pathways does UniProt protein P47989 belong to?
PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>
PREFIX ont: <http://ontology.dumontierlab.com/>
PREFIX uniprot: <http://lsrn.org/UniProt:>
SELECT ?gene ?pathway
WHERE {
uniprot:P47989 pred:isEncodedBy ?gene .
?gene ont:isParticipantIn ?pathway .
}
Note again that there is no “From” clause…
I have not told SHARE where to look for the
answer, I am simply asking my question

48. Enter that query into
SHARE

51. Two different
Two different providers of
providers of pathway
gene information
information (KEGG and
(KEGG & GO);
NCBI); were found &
were found & accessed
accessed

52. The results are all links to the original data
(The answer is IN the Web!)

53. Show me the latest Blood Urea Nitrogen and Creatinine levels
of patients who appear to be rejecting their transplants
(I showed you this query in ISoLA 2010… sorry for repeating myself  )
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 .
}

54. Show me the latest Blood Urea Nitrogen and Creatinine levels
of patients who appear to be rejecting their transplants
(I showed you this query in 2010… sorry for repeating myself!)
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 .
}

55. Likely Rejecter:
A patient who has creatinine levels
that are increasing over time
- - Mark D Wilkinson’s definition

56. Likely Rejecter:
…but there is no “likely rejecter”
column or table in our database…
only blood chemistry measurements
at various time-points

57. Likely Rejecter:
So the data required to answer this question
DOESN’T EXIST!

58. ?

59. Enter that query into
SHARE

60. SHARE “decomposes” the
Likely Rejector OWL class
into its constituent property restrictions

61. Each property restriction in the Class
is matched with a SADI Service
The matched SADI Service can
generate data that has that property

62. SHARE chains these SADI services
are into a workflow...
...the outputs from that workflow are
Instances (OWL Individuals)
of the Likely Rejector OWL Class

63. For example… SHARE utilizes SADI to discover
analytical services on the Web that do linear regression analysis;
required for the “increasing over time” part of the Class definition

64. VOILA!

65. SHARE examines the OWL Class
Gathers, from the Web, the ontologies that are
referenced by that Class
then uses those ontological properties to identify
which data-sources and analytical tools it must
access to create data matching that Class definition

66. OWL

67. The way SHARE builds the workflow varies
depending on the context of the query
(i.e. which data/ontologies it reads – Mine? Yours?)
and on what part of the query
it is trying to answer at any given moment
(which ontological concept is relevant to that clause)

68. And that brings us back to...

69. Web Science 2.0

70. 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).

71. derives and executes the following workflow automatically
using an OWL ontology that describes the biology

72. The analytical tools chosen for that
workflow were determined based on
context
even though the biological (ontological)
model driving their selection was the
same

73. i.e.
The published model is re-usable

74. i.e.
The published model is re-usable
In different contexts... by different researchers

75. Because the model IS the experiment
the published EXPERIMENT is re-usable!!
Simply point the same query at your own dataset...

76. The
scientific publication
is an
executable document!

77. Every component of the model
Every component of the input data
Every component of the output data
is a URL
Therefore the model, the question,
the experiment, and the results
are inherently IN the Web

78. Every component of the model
Every component of the input data
Every component of the output data
is a URL
The answer, and the knowledge derived from it,
is immediately available to Web search engines
and moreover, can instantly affect the outcome of
other Web Science experiments

80. You
Are Now
Here!!!

81. Change the way we think of “hypotheses”

82. In Web Science 2.0
Model what the world would “look like”
if your hypothesis were true
Then ask “is there any data that
fits that model?”

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

84. My New Home!

85. 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

86. 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

87. Microsoft Research