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 practice
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
The Seven Deadly Sins of BioinformaticsDuncan Hull
Keynote talk at Bioinformatics Open Source Conference (BOSC) Special Interest Group at the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2007) in Vienna, July 2007 by Carole Goble, University of Manchester.
Presentation for the BioAssist programmers face-to-face, Novemebr 17, 2008, Utrecht, The Netherlands. BioAssist is a nation-wide Bioinformatics support programme.
How SADI & SHARE help restore the Scientific Method to in silico scienceMark Wilkinson
This is my presentation to the Bio Open Source Convention (BOSC) in Boston, July 2010. I start with a brief status-update on the BioMoby project and then launch into a series of demonstrations of it's successor - SADI + SHARE. Rather than discussing how SADI/SHARE work, I focus the discussion on what role I think these technologies can play in bringing the traditional "scientific method" back into in silico biology.
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
The Seven Deadly Sins of BioinformaticsDuncan Hull
Keynote talk at Bioinformatics Open Source Conference (BOSC) Special Interest Group at the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2007) in Vienna, July 2007 by Carole Goble, University of Manchester.
Presentation for the BioAssist programmers face-to-face, Novemebr 17, 2008, Utrecht, The Netherlands. BioAssist is a nation-wide Bioinformatics support programme.
How SADI & SHARE help restore the Scientific Method to in silico scienceMark Wilkinson
This is my presentation to the Bio Open Source Convention (BOSC) in Boston, July 2010. I start with a brief status-update on the BioMoby project and then launch into a series of demonstrations of it's successor - SADI + SHARE. Rather than discussing how SADI/SHARE work, I focus the discussion on what role I think these technologies can play in bringing the traditional "scientific method" back into in silico biology.
The first part of an architectural research paper analyses current pre-design research methodologies in the clinical workplace and provides an evolutionary value added conclusion.
The first part of an architectural research paper analyses current pre-design research methodologies in the clinical workplace and provides an evolutionary value added conclusion.
Presentation to the J. Craig Venter Institute, Dec. 2014Mark Wilkinson
This is largely a compilation of various other talks that I have posted here - a summary of the past 3+ years of work on SADI/SHARE. It includes the (now well-worn!!) slides about SHARE, as well as some of the more contemporary stuff about how we extended GALEN clinical classes with richer semantic descriptions, and then used them to do automated clinical phenotype analysis. Also includes the slide-deck related to automated Measurement Unit conversion (related to our work on semantically representing Framingham clinical risk assessment rules)
So... for anyone who regularly follows my uploads, there isn't much "new" in here, but at least it's all in one place now! :-)
The Semantic Web - This time... its PersonalMark Wilkinson
My presentation on SADI, SHARE, CardioSHARE, and the new iConsent project. Presented to the faculty and students at Stanford Medical Informatics, Palo Alto, USA. May 14th, 2010.
How do we make the semantic web, and medical research, more personal? (both for the researcher and for the patient) I present some ideas we're exploring
Tales from BioLand - Engineering Challenges in the World of Life SciencesStefano Di Carlo
Prof. Alfredo Benso from SysBio Group @ Politecnico di Torino keynote presentation at ICIIBMS - IEEE International Conference on Intelligent Informatics and BioMedical Sciences, on Nov 26 2017 in Okinawa (Japan).
Data analysis & integration challenges in genomicsmikaelhuss
Presentation given at the Genomics Today and Tomorrow event in Uppsala, Sweden, 19 March 2015. (http://connectuppsala.se/events/genomics-today-and-tomorrow/) Topics include APIs, "querying by data set", machine learning.
I recently did a TED Ed talk on machine learning where I interviewed some of the top innovators in the field Including some of the creators of AlphaGo by Google's DeepMind and Members Of IBM's Watson team. I had a blast doing this talk and hope you enjoy listening to it also!
Mobile devices are now mainstream handheld computers providing access to computational power and storage that a decade ago was available only on desktop computers. In terms of chemistry informatics the majority of capabilities that were previously found only on desktop computers is fast migrating to mobile devices making use of the combination of powerful visualization capabilities, fast cloud-based calculations, websites optimized for the mobile platforms, and delivering “apps”. This presentation will provide an overview of how access to chemistry continues to be made increasingly mobile and specifically on how the Royal Society of Chemistry is contributing to this computing environment.
This is a short presentation about the FAIR Metrics Evaluator - software that automates the application of FAIR Metrics against a given resource, in order to determine its degree of "FAIRness"
An overview of the current functionality of the FAIR Evaluator - a framework for automating the evaluation of FAIRness of digital resources. The screenshots here are of the early strawman prototype, which is only available for use by the FAIR Metrics Authoring group at this time. Nevertheless, feedback on the functionality of the Evaluator would be welcome! We anticipate having a fully public version before August 2018.
This work is supported, in part, by the Ministerio de Economía y Competitividad grant number TIN2014-55993-RM
Quickly re-publish CSV/TSV files from existing repositories as FAIR Data with just a few mouse clicks!
You select the columns to "project" as Linked Data, and the associated ontology terms. The FAIR Projector Builder will create a FAIR Projector for you: a Triple Pattern Fragment server to provide the Linked Data; a published DCAT Distribution containing metadata about those triples and their source; and an RML model (syntactic and semantic of the triples, to aid in third-party discovery of this novel projection.
(current status - first prototype, not ready for public consumption)
-------
Thanks to the NBDC/DBCLS for sponsoring the hackathon series.
MDW also funded by Ministerio de Economía y Competitividad grant number TIN2014-55993-RM
Tech. session : Interoperability and Data FAIRness emerges from a novel combi...Mark Wilkinson
My presentation to OAI10 - CERN - UNIGE Workshop on Innovations in Scholarly Communication, 21-23 June 2017
University of Geneva.
https://indico.cern.ch/event/405949/contributions/2487823/
A description of the FAIR Accessor and FAIR Projector technologies: REST-compliant approaches to publishing FAIR Metadata and FAIR Data (respectively)
Spanish Ministerio de Economía y Competitividad TIN2014-55993-R
smartAPIs: EUDAT Semantic Working Group Presentation @ RDA 9th PlenaryMark Wilkinson
smartAPIs are an approach to the incremental, machine-aided, semantic annotation of Web APIs. Starting from existing, popular standards, we will provide enhanced tools for authoring ever-richer metadata, guided by global community knowledge encapsulated in ontologies, and aided by "smart suggestions" based on mining the metadata from previous API specifications.
The project is led by Michel Dumontier (Maastricht University). This presentation was given on his behalf by Mark Wilkinson (UPM, Madrid; Spanish Ministerio de Economía y Competitividad grant number TIN2014-55993-R)
IBC FAIR Data Prototype Implementation slideshowMark Wilkinson
Discussion about ways of achieving FAIRness of both metadata and data. Brute force approaches, and more elegant "projection" approaches are shown.
Relevant papers are at:
doi: 10.7717/peerj-cs.110 (https://peerj.com/articles/cs-110/)
doi: 10.3389/fpls.2016.00641 (https://doi.org/10.3389/fpls.2016.00641)
Spanish Ministerio de Economía y Competitividad grant number TIN2014-55993-R
FAIR Data Prototype - Interoperability and FAIRness through a novel combinati...Mark Wilkinson
This slide deck accompanies the manuscript "Interoperability and FAIRness through a novel combination of Web technologies", submitted to PeerJ Computer Science: https://doi.org/10.7287/peerj.preprints.2522v1
It describes the output of the "Skunkworks" FAIR implementation group, who were tasked with building a prototype infrastructure that would fulfill the FAIR Principles for scholarly data publishing. We show how a novel combination of the Linked Data Platform, RDF Mapping Language (RML) and Triple Pattern Fragments (TPF) can be combined to create a scholarly publishing infrastructure that is markedly interoperable, at both the metadata and the data level.
This slide deck (or something close) will be presented at the Dutch Techcenter for Life Sciences Partners Workshop, November 4, 2016.
Spanish Ministerio de Economía y Competitividad grant number TIN2014-55993-R
Building SADI Services Tutorial - SIB Workshop, Geneva, December 2015Mark Wilkinson
The primary slide deck for the SADI tutorial. We explain the motivation, simple SADI services, more complex SADI services, and then do a detailed walk-through of building a service, including the Perl service code and examples of service invocation at the command line, and using the SHARE client. You will want to look at the sample data/queries in this slide deck: http://www.slideshare.net/markmoby/sample-data-and-other-ur-ls-55737183 and the example service code in this slide deck: http://www.slideshare.net/markmoby/example-code-for-the-sadi-bmi-calculator-web-service?related=1
Example code for the SADI BMI Calculator Web ServiceMark Wilkinson
Two versions of the code for the SADI Web Service demonstrated at the Using the Semantic Web for faster (Bio-)Research workshop hosted by the Swiss Institute for Bioinformatics, Geneva, December, 2015. The first version of the code is a bare-bones service that consumes individuals with height and weight and returns individuals with a BMI. The second piece of code is functionally identical to the first, but highlights the small changes required to make the service a NanoPublisher (NanoPublishing services respond to Accept n-quads HTTP headers by returning NanoPublications, rather than just a stream of triples)
Perl code for a SADI service that calculates BMI. The first panel is the code for a traditional SADI service, the second panel highlights the minor changes required to convert the service into a service that outputs NanoPublications.
Luke McCarthy's tutorial - originally created for the CBRASS Project, funded by CANARIE.
The slideshow takes you though the design of a SADI Service, the considerations when creating service input and output classes (where DL reasoning is used for matchmaking), and how SADI fits with other initiatives such as SAWSDL
Data FAIRport Prototype & Demo - Presentation to Elsevier, Jul 10, 2015Mark Wilkinson
A discussion and demonstration of a functional Data FAIRport, using W3C's Linked Data Platform, Ruben Verborgh's Linked Data Fragments, and Hydra's hypermedia controlled vocabularies. This is the output of the "Skunkworks" working group of the larger Data FAIRport project (http://datafairport.org).
Presentation of the early prototype of "FAIR Profiles" - an example of the proposed DCAT Profile, proposed by the DCAT working group (but AFAIK never implemented). This prototype emerged from the activity of the "Skunkworks" group, from the Data FAIRport project.
Enhancing Reproducibility and Transparency in Clinical Research through Seman...Mark Wilkinson
We were interested in whether we could model well-established clinical risk guidelines in OWL, and use these to automatically classify patient data v.v. "risk" (e.g. using the Framingham risk categories). What we ended-up doing, however, was wandering down a very interesting path of attempting to model clinical intuition! This reports the first phase of the experiment. A subsequent SlideShare will give part II of this investigation.
This is the work of Soroush Samadian, Ph.D. Candidate at the University of British Columbia Bioinformatics Graduate Programme.
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.
Evaluating Hypotheses using SPARQL-DL as an abstract workflow language to cho...Mark Wilkinson
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.
SWAT4LS 2011: SADI Knowledge Explorer Plug-inMark Wilkinson
my presentation of the SADI plug-in to the IO Informatics' Knowledge Explorer. Presented at SWAT4LS (Semantic Web Applications and Tools for Life Sciences), London, UK, December, 2011. It describes how we resolve identifiers to semantic metadata in a variety of ways in order to boot-strap the semantics required to do service discovery and matching. It also describes how we convert OWL classes into approximately matching SPARQL queries, and store these queries in the SADI registry such that, after service discovery, it is simple to extract the data a service requires as its input.
SADI in Perl - Protege Plugin Tutorial (fixed Aug 24, 2011)Mark Wilkinson
IMPORTANT CORRECTION TO THIS SLIDESHOW WAS MADE August 24, 2011. How to use the Protege SADI plugin to generate SADI-compliant semantic web services. Created for the 2011 DBCLS BioHackathon. Credits to Mark Wilkinson, Benjamin Vandervalk, Luke McCarthy, Edward Kawas.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
Web Science, SADI, and the Singularity
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.
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
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...
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
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)
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
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...
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
60. Two different
Two different providers of
providers of pathway
gene information
information (KEGG and
(KEGG & GO);
NCBI); were found &
were found & accessed
accessed
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
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!
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)
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
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
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
116.
117. Please join us!
SADI and SHARE are Open-Source projects
http://sadiframework.org
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