Verification of systems biology research in the age of ... · The sbv IMPROVER project team (2013)....
Transcript of Verification of systems biology research in the age of ... · The sbv IMPROVER project team (2013)....
Verification of systems biology research in the age of collaborative competition
May 7th, 2015
Dr Julia Hoeng, Philip Morris International
Prof. Manuel Peitsch, Philip Morris International
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Outline
• 21st Century Science
• sbv IMPROVER methodology
• sbv IMPROVER challenges
• Tools and strategies for Open Innovation
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Outline
• 21st Century Science – Need for Open
Innovation and Method Verification
• sbv IMPROVER methodology
• sbv IMPROVER challenges
• Tools and strategies for Open Innovation
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Scientific
knowledge
Biological
insight
Toxicity
testing
Mode of
action
identification
Pharmacological
assessment
Biomarker
discovery
Patient
stratification
Biological Network Models to Empower ‘Systems’ -Biology, -Toxicology, and -Medicine
Scientific
data
Structured knowledge
in biological network
models
Tailored algorithms
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Research Verification in the Age of Collaborative-competition
• Rigorous scrutiny of scientific
research based on communities
involvement
• A crowd sourcing approach of
challenge-based evaluation of
scientific methods could be a
potential alternative to the peer
review system.
• Our goal is to develop a robust
methodology which verifies
systems biology-based
methods and results in the
context of industrial and
academic research
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Develop a robust methodology that verifies systems biology-based approaches
Meeting the needs of industry –The role of sbv IMPROVER
The self-assessment trap: can we all be
better than average?
Researchers wishing to publish their methods are
usually required to compare their methods against others
Authors’ method tends to be the best in an unreasonable
majority of cases
Selective reporting of performance: inadvertent or
disingenuous
Choice of only one, best metric
Mol Syst Biol. 2011 Oct 11;7:537. doi: 10.1038/msb.2011.70.
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Concepts of Community-based Efforts
• Crowd-sourcing: A natural evolution of web technologies led to the
development of distributed problem-solving. Challenges are broadcasted
to potential interested stakeholders (solvers). The winning participants are
rewarded either with monetary awards, prizes, certificates, or with
recognition.
• Collaboration by Competition: The scientific community sought to
understand the limitations and comparative advantages of their methods
by challenging model developers to make blind predictions on previously
unseen data in a competitive framework.
• The community appreciates the successful methods which grow in
credibility. Therefore, consideration of the scientific community is one of
the forces that shape what is currently considered as the way to do the
science right
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Outline
• 21st Century Science
• sbv IMPROVER methodology
• sbv IMPROVER challenges
• Tool and strategies to support crowd
engagement
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Community-Wide Prediction & Verification Projects in Science
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Analysis of Community Approaches
Common principles in community
efforts
• Assessment of a prediction by an
impartial, outside party is a more
rigorous model verification than self-
assessment.
• The responses of the community to a
prediction challenge can build
consensus in the community regarding
the most constructive methods for the
task.
• Voluntary participation in a prediction
challenge is driven by a number of
incentives.
• Harvest the knowledge and intellectual
power of the community to improve
methods, algorithms and experimental
design.
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Companies Offering Services as Crowdsourcing Brokers
Type of
work
Company Description of company Project types Industry clients
Res
earc
h a
nd
Des
ign
InnoCentive Provides an open innovation
marketplace that matches a global
network of crowdworkers with
research and development
challenges submitted by
organisations.
Research and
development problems in
engineering, computer
science, mathematics,
chemistry, life sciences,
physical sciences and
business.
Commercial, government and non-
profit organisations; e.g., Procter
& Gamble, Dow AgroSciences, the
Air Force Research Lab, NASA, and
the Rockefeller Foundation.
Presans Provides an open innovation
marketplace that matches expertise
and business via problem-solving
competitions in various industrial
sectors.
Research and
development problems in
defence, space and
aeronautics, car
manufacturing, advanced
manufacturing and
materials, energy and
resources, and food and
agriculture.
Mostly large organisations,
universities and research
organisations; e.g., École
Polytechnique and CNRS.
IdeaConnection Provides an open innovation
marketplace that matches a global
network of crowdworkers with
research and development
challenges submitted by
organisations.
Research and
development problems in
various industries.
Small and large enterprises.
Mic
ro
-tas
ks Amazon
Mechanical Turk
Provides a crowdsourcing Internet
marketplace. Works on commission.
Tasks that computers are
currently unable to do,
such as transcribing,
rating and image tagging.
Individuals and businesses
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Crowdsourcing Advantages
• Many contributors with independent methods / knowledge
• Different solutions tackle various aspects of a complex problem
• The combination of solutions often outperforms the best performing
submissions and is extremely robust “Wisdom of Crowds”
• Nucleates a community around a given scientific problem
• Allows for unbiased benchmarking
• Establishes state-of-the-art technology and knowledge in a field
• Complements the classical peer-review process
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sbv IMPROVER Fills a Gap in Research Quality Assessment
Leverages the collective expertise of the scientific community to provide the best answers
Anonymous as
Known
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Divide a Research Workflow into Verifiable Building Blocks
Building blocks support each other towards a final goal
Each building block is verifiable by a challenge
Example of a Research Pipeline – In vivo Inhalation Study
sbv IMPROVER team. 2011. Verification of systems biology research in the age of collaborative competition.
Nature Biotechnology 29: 811-815.
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Participation and Collaboration with Experts
• The development of a Systems Biology Verification (SBV) method is a major scientific
challenge which requires participation and collaboration with experts from academia,
industry and other interested parties.
• To secure a wide application of the SBV method and to meet the complex requirements of
developing and applying a standard scientific method, we have built a multidisciplinary
research team, made-up of leading scientists and experts across academia and from
multiple industries
Project
Team
Special
Interest Group
Participants
Subject Matter Expert
(SME)
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Benefits of Strategic Crowd Engagement
Enhanced dialogue
with the scientific
community
New standards for
validating and
publishing big datasets
in Toxicology and
Biology
Benchmarking
methods and results
with the community
of expertsPublications in
strategically important
journals, usually
inaccessible to individual
articles
Publications and
presentations
Full transparency of
the research process
Scientific
credibility
and
confidence
Joint discoveries and
open innovation
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Outline
• 21st Century Science
• sbv IMPROVER methodology
• sbv IMPROVER challenges
• Tools and strategies for Open Innovation
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Training
datasets
Test
datasets
Symposium
Crowd sourcing brings new ideas
and leverages the wisdom of crowds
Clear challenge description and forum
discussion in a user friendly website
The Elements of a Challenge
Challenge
Define the
Question
Score Challenge and Present Results
Engage Crowds to Solve Challenge
Collect Data and
‘Gold Standard’
Narrow the
Scope
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The Diagnostic Signature Challenge
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Outcome of the Diagnostic Signature Challenge
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The Species Translation Challenge
The objective of the Species Translation Challenge was to:
• Identify a function which maps measurements derived from systematic perturbations in
one species to another
• Understand the system boundaries of the translatability concept
• Quantify the translatability between species
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Outcome of the Species Translation Challenge
• To learn more about the outcome of the Species Translation Challenge the following articles have been
published in the Bioinformatics Journal:
– Understanding the limits of animal models as predictors of human biology: lessons learned from the
sbv IMPROVER Species Translation Challenge
(Bioinformatics Overarching Paper, 17 September 2014)
– Inter-Species Pathway Perturbation Prediction via Data Driven Detection of Functional Homology
(Bioinformatics, 4 August 2014)
– Predicting protein phosphorylation from gene expression: Top methods from
the IMPROVER Species Translation Challenge
(Bioinformatics, 23 July 2014)
– Inter-species prediction of protein phosphorylation in the
sbv IMPROVER Species Translation Challenge
(Bioinformatics, 3 July 2014)
• To learn more about the data set used during the Species Translation Challenge the following article has
been published in Scientific Data:
– The species translation challenge - A systems biology perspective on human and rat bronchial
epithelial cells. (Scientific Data, 10 June 2014)
This work constitutes a proof of principle that the molecular responses
induced by active substances in an in vitro system are to some extend
predictive of the responses observed in the same system of another species
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sbv Improver team. 2013. On Crowd-verification of Biological Networks. Bioinformatics and biology insights 7:
307-325.
Challenge 3 – Biological Networks Verification Challenge
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Network Verification Challenge
• The first NVC took place October 2013 –
February 2014
– Participants reviewed biology in networks
and approved or rejected evidence
– Participants added biology to the networks
in the form of additional evidence or new
edges
– The aim is to build a consensus around what
parts of the networks are accurate, incorrect
or incomplete.
• NVC2 is ongoing: started February 2014
and closes April 2015
https://sbvimprover.com/challenge-3/challenge
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The sbv IMPROVER project team (2013). On Crowd-verification of Biological Networks. Bioinformatics and Biology Insights 2013:7 307-325.
NVC2 (ongoing): February 2014 April 2015 Mid-2015NVC (finished):
Steps in the sbv IMPROVER Network Verification Challenge
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Number of Publications that Mention the Word “Jamboree”.
0
10
20
30
40
50
60
70
80
90
1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Nu
mb
er
of
Pu
blic
atio
ns
add
ress
ing
"jam
bo
ree
"
Scoutsjamborees
Technicaljamborees
Rock'n rolljamboree
Gene findingjamboree
Genome annotation jamborees
Undergradjamboree
Networkreconstruction
jamborees
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Outcome of The first Network Verification Challenge in numbers
150 participants
from 18 countries
Number of Participants1 10 20
• The sbv IMPROVER project team and the
Challenge Best Performers. Enhancement of
COPD biological networks using a web-based
collaboration interface. F1000Research. 2015; 4:
32.
• Binder J, Boue S, DI Fabio A, et al. Reputation-
based collaborative network biology. Pacific
Symposium on Biocomputing Pacific Symposium
on Biocomputing. 2014, p. 270-81.
2 publications
451 new edges
2,456 votes
885 new pieces of evidence
Activity during the open phase
(10/2013 – 02/2014)
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Outline
• 21st Century Science
• sbv IMPROVER methodology
• sbv IMPROVER challenges
• Tools and strategies for Open Innovation
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The Network Verification Challenge Principles
Motivation
Jamboree
Marketing
Platform
Crowd-
sourcing
System
Biology
Scientific community to verify
biological network built by PMI
2000 nodes (lung)
8000 edges
80000 pieces of evidence
Leaderboard & reputation
Travel bursaries
Scientific event
Academic articles
Scientific event
Used to “curate”
activities
Top speakers
Networking opportunities
Ads
Conferences
Videos & webinars
Publications
Ambassadors
Intuitive
Interactive
Social
Network
Verification
Challenge
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What Motivates Scientists to Join sbv IMPROVER?
Be part of a pioneering community, working together to improve how scientific
research is verified
Drive innovation in science with creative crowd-sourced solutions
Create smarter solutions to complement peer review with collaborative crowd-sourcing
Get peer recognition and self-esteem
Engage closely and network with experts around the world
Contribute and work towards consensus in the scientific community
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Geographical Tracking to Engage More Participants
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A Platform to Easily Verify and Refine the Networks and Supporting Evidence
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Display News Feed to Encourage Participation
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Gamification Principle
• Used widely to support user engagement and enhance positive patterns in
service use
• Recently also reflected in academic use
• Results in increased motivation and engagement in the learning and
enjoyment over the tasks
Hamari J, Koivisto J, Sarsa H (2014) Does Gamification Work?--A Literature Review of Empirical Studies on Gamification. In:
System Sciences (HICSS), 2014 47th Hawaii International Conference on. IEEE, p 3025-3034
Domínguez A, Saenz-De-Navarrete J, De-Marcos L et al. (2013) Gamifying learning experiences: Practical implications and
outcomes. Computers & Education 63:380-392
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Gain Points and Increase Your Rank in the LeaderBoard
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Marketing & Advertising - How Wise is the Crowd?
Promotional materials were
translated into different languages:
Spanish, Chinese, Russian,
Japanese
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Compete. Collaborate. Contribute. Join us in Barcelona in June 2015.
https://sbvimprover.com/discover
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Jamboree Meeting in Montreux, Switzerland
As published in Nature, 8 May 2014, page 127
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sbv IMPROVER: a Brand and a Community
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Summary - sbv IMPROVER at a Glance
Aims to provide a measure of quality control in R&D by identifying
the building blocks that need verification in a complex industrial
research pipeline
Is a robust, respected and recognized methodology which verifies
systems biology-based methods and results
Complements the classical peer review system
Has potential application in the areas of risk assessment where a
more profound and insightful mechanistic understanding (e.g.,
biomarkers) would make product development and assessment
more efficient and reliable
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sbv IMPROVER: Conference Attendance 2015
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Acknowledgements to a Global Team
ProtAtOnce
OrangeBus
PMI
IBM GBS
Advantage Integral
IBM
Research
ADS
Selventa
IBM
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The sbv IMPROVER project, the website and the Symposia are part of a
collaborative project designed to enable scientists to learn about and
contribute to the development of a new crowd sourcing method for
verification of scientific data and results. The current challenges, website
and biological network models were developed and are maintained as part
of a collaboration among Selventa, OrangeBus and ADS. The project is led
and funded by Philip Morris International. For more information on the focus
of Philip Morris International’s research, please visit www.pmi.com
Thank you for your Attention
https://sbvimprover.com/discover
To learn more, visit
Questions? Contact Us
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BACKUP
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Why do we need to verify that it is possible to infer clinical phenotype from genomics data?
A few success stories of gene expression based biomarkers in clinical use
MammaPrint (breast cancer recurrence assay)
70-gene profile; requires fresh tissue
Oncotype Dx (breast cancer recurrence assay)
21-gene profile; works on both fresh and fixed tissue
Counter-balanced by a few failure stories of gene expression based biomarkers in
clinical use
Potti et al, Nat Med (2006) claimed to identify genomic signatures for drug response.
Three clinical trials begun in 2007, 2008 for lung and breast cancer. The research
was later deemed statistically flawed and at least 10 high profiled publications
were retracted and the clinical trials stopped.
Amgen scientists tried to confirm 53 landmark papers in pre-clinical oncology
research: Only 6 (11%) were confirmed.
Bayer HealthCare reported that only about 25% of published preclinical studies
could be validated.