the same story as usual, but with a bit more context (why it is absolutely necessary to move science in this direction). Presented to University of Potsdam, Germany, and the University of New Brunswick, Canada in December, 2012.

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
Multiple recent surveys of high-throughput biology
reveal that upwards of 50% of published studies
are not reproducible
- Baggerly, 2009
- Ioannidis, 2009

3. Context
“the most common errors are simple,
the most simple errors are common”
- Baggerly, 2009

4. Context
These errors pass peer review
The researcher is unaware of the error
The process that led to the error is not recorded
Therefore it cannot be detected during peer-review

5. Context
Discovery of such errors have resulted in retractions
and even shut-down clinical trials

6. Context
In March, 2012, the US Institute of Medicine said
“Enough is enough!”

7. Context
Institute of Medicine Recommendations
For Conduct of High-Throughput Research:
1. Rigorously-described, -annotated, and -followed data
management procedures
2. “Lock down” the computational analysis pipeline once it
has been selected
3. Publish the workflow in a formal manner, together with the
full starting and result datasets
Evolution of Translational Omics Lessons Learned and the Path Forward. The
Institute of Medicine of the National Academies, Report Brief, March 2012.

8. Achieving these recommendations
requires integration of existing technologies
and invention of new ones

9. 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.”
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.

10. “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.

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

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

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

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

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

16. Web Science 2.0

17. 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

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

19. Brief Digression
“in” the Web??

20. How we use
The Web today

22. 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...

23. The Web is not a pigeon!

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

25. Resume Speed

26. 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

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

28. This is the study we chose:

29. 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).

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

31. “Pseudo-code” Abstracted Workflow
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

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

33. 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

34. 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)

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

36. 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

37. The tricky bit is...
In the abstract, the
search for homology is
“generic” – ANY
Protein, ANY model
system
But when the machine
does the experiment, it
must use specific of
resources because the
answer requires taxon:4932 a i:ModelOrganism1 . # yeast
information from two taxon:7227 a i:ModelOrganism2 . # fly
declared species

38. 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

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

41. There are four very cool things about what you just saw...

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

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

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

45. There are four very cool things about what you just saw...
The choice of tool-selection was guided
by the encoded knowledge of domain-experts
worldwide

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

47. How did we do that?

48. A “Smart” Biomedical Resource Representation System

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

50. Demo #1

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

52. How can we query that database?

54. 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
}

55. 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...

56. Enter that query into
SHARE

57. Click “Submit”...

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

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

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

61. 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 .
}

62. 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 .
}

63. 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

64. Enter that query into
SHARE

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

68. The results are all links to the original data

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

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

71. Demo #2
An example from the Clinical domain

72. 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 .
}

73. 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 .
}

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

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

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

77. ?

78. Enter that query into
SHARE

79. Now…
Two “magical” events occur…

80. 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

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

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

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

84. VOILA!

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

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

87. Ontologies

88. 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.)

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

90. 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”

91. 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

92. 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

93. 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)

94. Data exhibits “late binding”

95. 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

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

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

98. 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)

99. 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)

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

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

102. And that brings us to...

103. 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).

104. We built a model of the proposed answer

105. Our system converted the model into the experiment

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

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

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

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

110. The publication is an
executable document!

111. 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

112. 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

114. You
Are Now
Here!!!

115. Final thoughts

116. An experiment... based on a hypothesis

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

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

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

120. Change the way we think of “hypotheses”

121. 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?”

122. Like the blind men examining an elephant
Seemingly different aspects of research
when viewed from the perspective of Web Science
become the same “thing”
The Model

123. 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

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

126. My New Home!

127. 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

128. 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

129. Microsoft Research