web science, sadi, and the singularity
DESCRIPTION
Science in the Web, from hypothesis to result. Publishing in silico experiments IN the Web allows us to immediately and precisely disseminate new knowledge that can affect other Web Science experiments. This is the "singularity" where a new discovery is immediately put into practiceTRANSCRIPT
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Web Science 2.0
Conducting in silico research in the Webfrom hypothesis to publication
Mark Wilkinson
Isaac Peral Senior Researcher in Biological InformaticsCentro de Biotecnología y Genómica de Plantas, UPM, Madrid, Spain
Adjunct Professor of Medical Genetics, University of British ColumbiaVancouver, BC, Canada.
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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, USAJune 22, 2012.
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The Healthcare Singularity and the Age of Semantic Medicine, Michael Gillam, et al, The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey (Editor), 2009Slide Borrowed with Permission from Joanne Luciano, Presentation at Health Web Science Workshop 2012, Evanston IL, USAJune 22, 2012.
“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...)
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The technology required to achieve this does not yet exist
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Scientific research would have to be conducted within a medium that
immediately interpreted and disseminated the results...
You Are
Here
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...in a form that immediately (actively!) affected the research of others...
You Are
Here
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...without requiring them to be aware of these new discoveries.
You Are
Here
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I’d like to show you how close we now are to this vision
and how we got there
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Web Science 2.0
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We wanted to duplicatea real, peer-reviewed, bioinformatics analysis
simply by building a model in the Webdescribing what the answer
(if one existed)
would look like
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...the machine had to make every other decision
on it’s own
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Brief Digression
“in” the Web??
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How we use The Web today
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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...
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The Web is not a pigeon!
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To achieve this vision
We must learn how to do research IN the Web
Not OVER the Web
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Resume Speed
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We wanted to duplicatea real, peer-reviewed, bioinformatics analysis
simply by building a model in the Webdescribing what the answer
(if one existed)
would look like
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...the machine had to make every other decision
on it’s own
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This is the study we chose:
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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).
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Original Study Simplified
Using what is known about interactions in fly & yeast
predict new interactions with your human protein of interest
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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
Abstracted
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Modeling the answer...
OWL
Web Ontology Language (OWL) is the language approved by the W3C
for representing knowledge in the Web
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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 Weband borrows ideas from other models published in The Web
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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)
Modeling the answer...
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Publish our OWL model of a Probable Interactor
in the Web
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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 . # humanuniprot:Q9UK53 a i:ProteinOfInterest . # ING1taxon:4932 a i:ModelOrganism1 . # yeasttaxon:7227 a i:ModelOrganism2 . # fly
Running the Web Science Experiment
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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
information from two different species
taxon:4932 a i:ModelOrganism1 . # yeasttaxon:7227 a i:ModelOrganism2 . # fly
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PREFIX i: <http://sadiframework.org/ontologies/InteractingProteins.owl#>
SELECT ?proteinFROM <file:/local/workflow.input.n3>WHERE {
?protein a i:ProbableInteractor .}
This is the question we ask:(the query language here is SPARQL)
The reference (URL) to our OWL model of the answer
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Our system then derives (and executes) the following workflow automatically
These are differentWeb services!
...selected at run-time based on the same model
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There are three very cool things about what you just saw...
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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)
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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
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There are three very cool things about what you just saw...
The workflow it created (i.e. the services chosen)
differed depending on context
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We got the answer
“simply” by designing a model of the answer!
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How did we do that?
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Two novel technologies
developed by my laboratory members
over the past 8 years
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A “Smart” Biomedical Resource Representation System
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A Web application that answers SPARQL-DL queries
Query-answering Enhanced by SADI
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Demo #1
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Imagine a “virtual database”
all of the data from all databases
+result of
every conceivable analysis
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How can we query that database?
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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 }
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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...
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Enter that query into SHARE
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Click “Submit”...
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...and in a few seconds you get your answer.
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The query results are live hyperlinksto the respective Database or images
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Neither SADI nor SHARE
know anything about
plant biology or genetics
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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 .
}
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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 .
}
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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
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Enter that query into SHARE
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Two different providers of gene information (KEGG & NCBI); were found & accessed
Two different providers of pathway information (KEGG and GO); were found & accessed
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The results are all links to the original data
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Neither SADI nor SHARE
know anything about
proteins or biochemical pathways
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Recapwhat we just saw
We posed, and answered ~complex multi-database queries
WITHOUT A DATA WAREHOUSE
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An example from the Clinical domain
Demo #2
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Show me the latest Blood Urea Nitrogen and Creatinine levelsof 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 ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE {
?patient rdf:type patient:LikelyRejecter .?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat .
}
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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 ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE {
?patient rdf:type patient:LikelyRejecter .?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat .
}
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Likely Rejecter:
A patient who has creatinine levelsthat are increasing over time
- - Mark D Wilkinson’s definition
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Likely Rejecter:
…but there is no “likely rejecter” column or table in our database…
only blood chemistry measurementsat various time-points
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Likely Rejecter:
So the data required to answer this questionDOESN’T EXIST!
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?
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Enter that query into SHARE
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Now…
Two “magical” events occur…
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The machine decides
by itself
that it needs to do a Linear Regression analysis
on the blood creatinine measurementsin order to answer your question
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The machine decides
by itself
how and where that analysiscan be done
and does it automatically!
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http://www.impactlab.net/2009/03/22/improve-your-brain-power/
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The SHARE system utilizes SADI to discover analytical services on the Web that do linear regression analysis
and sends the data to be analysed
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VOILA!
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Neither SADI nor SHARE
know anything about
blood chemistry, or mathematics
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So how does the machine know what to do??
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Ontologies
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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.)
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So we create ………….ontologies about biology
and health
We* publish them on the Web
* We… or anybody! Anybody can publish an ontology!
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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”
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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
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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
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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)
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Data exhibits “late binding”
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Late binding:
“purpose and meaning”of the data is
not determined untilthe moment it is required
a.k.a The “semantics” of the data
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Benefitof late binding
Data is amenable toconstant re-interpretation
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Example?
Blood Creatinine measurements
were not dictated to be (only)
Blood Creatinine measurements
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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)
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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)
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Benefitof late binding
Data is amenable toconstant re-interpretation
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http://www.flickr.com/people/faernworks/
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And that brings us to...
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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).
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Our system then derives (and executes) the following workflow automatically
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The analytical tools chosen for that workflow changed depending on
context
even though the biological model driving their selection was the same
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i.e.
The published model is re-usable
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i.e.
The published model is re-usable
In different contexts... By different researchers
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and because the model IS the experiment
the published EXPERIMENT is re-usable!!
simply point the same query at your own dataset...
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The publication is an executable document!
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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
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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
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You Are NowHere!!!
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Final thoughts
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An experiment... based on a hypothesis
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An experiment... based on a hypothesis
now modeled in OWL
. . .
. . .
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Does this OWL Class represent the Hypothesis?
I think it does!
. . .
. . .
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We modeled the answer......but the answer was hypothetical
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Change the way we think of “hypotheses”
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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?”
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Like the blind men examining an elephant
Seemingly different aspects of Web Science researchare embodied in/derived from the same “thing”
The OWL Model
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Our vision of Web Science 2.0
Hypothesis
Ontology
Query
Workflow
Result
These can be automatically derived through provenance information during workflow execution
Materials & Methods
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Please join us!
SADI and SHARE are Open-Source projects
http://sadiframework.org
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My New Home!
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Luke McCarthy – Lead Dev.Everything...
Benjamin VanderValk SHARE & SADI & Experimental modeling & myHeath Button
Soroush Samadian Cardiovascular data modeling and queries
University of British Columbia
Edward Kawas SADI Service auto-generator
Ian WoodExperimental modeling project
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U of New Brunswick
Dr. Chris BakerAlexandre Riazanov
Carleton University
Dr. Michel DumontierMarc-Alexandre NolinLeonid ChepelevSteve EtlingerNichaella KiethJose Cruz
C-BRASS Collaborators at other sites
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Microsoft Research