The document discusses the concept of Web Science 2.0, highlighting advancements in conducting in silico research that allow for immediate application of discoveries in scientific practice. It emphasizes the use of ontologies, particularly the Web Ontology Language (OWL), to automate workflows in bioinformatics, enabling machines to interpret and interact with data without needing human intervention. The paper illustrates this through examples of complex multi-database queries and the adaptability of published models for re-use in various research contexts.

1. Web Science 2.0
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. I‟d like to show you how close
we now are to this vision
and how we got there

9. Web Science 2.0

10. 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

11. ...the machine had to make
every other decision
on it‟s own

12. Brief Digression
“in” the Web??

13. How we use
The Web today

15. By clicking here you cause this incredibly
powerful computational tool called The Web
to retrieve a chunk of text and images that
can only be understood by a human...

16. The Web is not a pigeon!

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

18. Resume Speed

19. 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

20. ...the machine had to make
every other decision
on it‟s own

21. This is the study we chose:

22. 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).

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

24. 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

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

26. Modeling the answer...
Note that every word in
this diagram is, in reality, a
URL (because it is OWL)
The model of the answer is
published in The Web
and borrows ideas from
other models published in
The Web

27. 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)

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

29. Running the Web Science 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

30. 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 a variety of
resources because the
answer requires taxon:4932 a i:ModelOrganism1 . # yeast
information from two taxon:7227 a i:ModelOrganism2 . # fly
different species

31. 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

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

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

35. 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)

36. 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

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

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

39. How did we do that?

40. Two novel technologies
developed by my laboratory members
over the past 8 years

41. A “Smart” Biomedical Resource Representation System

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

43. Demo #1

44. Imagine a “virtual database”
all of the data
from all databases
+
result of
every conceivable analysis

45. How can we query that database?

47. 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
}

48. 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...

49. Enter that query into
SHARE

50. Click “Submit”...

51. ...and in a few seconds you get your answer.

52. The query results are live hyperlinks
to the respective Database or images

53. Neither SADI nor SHARE
know anything about
plant biology or genetics

54. 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 .
}

55. 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 .
}

56. 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

57. Enter that query into
SHARE

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

61. The results are all links to the original data

62. Neither SADI nor SHARE
know anything about
proteins or biochemical pathways

63. Recap
what we just saw
We posed, and answered
~complex multi-database queries
WITHOUT A DATA WAREHOUSE

64. Demo #2
An example from the Clinical domain

65. 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 .
}

66. Show me the latest Blood Urea Nitrogen (BUN) 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 .
}

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

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

69. Likely Rejecter:
So the data required to answer this question
DOESN‟T EXIST!

70. ?

71. Enter that query into
SHARE

72. Now…
Two “magical” events occur…

73. The machine decides
by itself
that it needs to do a
Linear Regression analysis
on the blood creatinine measurements
in order to answer your question

74. The machine decides
by itself
how and where that analysis
can be done
and does it automatically!

75. http://www.impactlab.net/2009/03/22/improve-your-brain-power/

76. The SHARE system utilizes SADI to discover
analytical services on the Web that do linear regression analysis
and sends the data to be analysed

77. VOILA!

78. Neither SADI nor SHARE
know anything about
blood chemistry, or mathematics

79. So how does the machine know
what to do??

80. Ontologies

81. Ontologies explicitly define the kinds of
things that (can) exist…
…and what those things are “like”
i.e. what properties they have
(color, weight, shape, texture, temperature, “state”)
and what relationships they have to one another
(inside-of, adjacent-to, part-of, binds-to, controls, inhibits,
degrades, etc.)

82. So we create ………….
ontologies about biology
and health
We* publish them on the Web
* We… or anybody! Anybody can publish an ontology!

83. My definition of a Likely Rejecter 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”

84. Our ontology refers to other ontologies (possibly published by other people)
to learn about what the properties of “regression models” are
e.g. that regression models have slopes and intercepts
and that slopes and intercepts have decimal values

85. SHARE examines the query
Looks on the Web for ontologies that describe the
problem it is trying to solve, and “reads” them
then uses that “knowledge” to figure out which
data-sources and analytical tools it needs
to answer the query

86. The way SHARE “interprets” data varies
depending on the context of the query
(i.e. which 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)

87. Data exhibits “late binding”

88. Late binding:
“purpose and meaning”
of the data is
not determined until
the moment it is required
a.k.a The “semantics” of the data

89. Benefit
of late binding
Data is amenable to
constant re-interpretation

90. Example?
Blood Creatinine measurements
were not dictated to be (only)
Blood Creatinine measurements

91. Example?
The data had the „qualities/properties‟ that
allowed one machine to interpret
that they were Blood Creatinine measurements
(e.g. to determine which patients were rejecting)

92. Example?
But the data also had the „qualities/properties‟ that
allowed another machine to interpret them as
Simple X/Y coordinate data
(e.g. the Linear Regression calculation tool)

93. Benefit
of late binding
Data is amenable to
constant re-interpretation

94. http://www.flickr.com/people/faernworks/

95. And that brings us to...

96. 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).

97. Our system then derives (and executes) the following workflow automatically

98. The analytical tools chosen for that
workflow changed depending on
context
even though the biological model driving
their selection was the same

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

100. i.e.
The published model is re-usable
In different contexts... By different researchers

101. and because the model IS the experiment
the published EXPERIMENT is re-usable!!
simply point the same query at your own dataset...

102. The publication is an
executable document!

103. Every component of the model
Every component of the input data
Every component of the output data
is a URL
Therefore the question, the experiment, and the
answer, are immediately published IN the Web

104. 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 search engines
and moreover, can affect the outcome of other
Web Science experiments

106. You
Are Now
Here!!!

107. Final thoughts

108. An experiment... based on a hypothesis

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

110. Does this OWL Class represent the Hypothesis?
I think it does!
...
...

111. We modeled the answer...
...but the answer was hypothetical

112. Change the way we think of “hypotheses”

113. 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?”

114. Like the blind men examining an elephant
Seemingly different aspects of Web Science research
are embodied in/derived from the same “thing”
The OWL Model

115. Our vision of Web Science 2.0
Hypothesis Query
Workflow
Ontology Result
Materials &
Methods
These can be automatically derived through
provenance information during workflow execution

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

118. My New Home!

119. 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

120. 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

121. Microsoft Research