My talk at the Open PHACTS last ever project meeting in Vienna 2016 where i was asked to talk about the challenges we addressed in open phacts with semantic web technology and what still needed to be done.

1. SciBite
http://openphacts.org http://scibite.com

2. •
•
• HTTP://OPENPHACTS.ORG
• HTTP://SCIBITE.COM

3. Meeting of multiple
pharma to discuss key
issues, lead to the
Open PHACTS IMI Call
text

4. http://readwrite.com/2009/12/15/twitters_top_10_tech_trends_of_2009
Even with a lot of cash,
success is not a given

5. Was a time of massive
increase in data
science / big data

6. So if we were planning
Open PHACTS in 2016,
what would be on my
mind?

7. •
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9. A second concern held by some is that a new class of research person will
emerge — people who had nothing to do with the design and execution of
the study but use another group’s data for their own ends, possibly stealing
from the research productivity planned by the data gatherers, or even use the
data to try to disprove what the original investigators had posited. There is
concern among some front-line researchers that the system will be taken over
by what some researchers have characterized as “research parasites”
Rather than labelling
people parasites, lets
make the tools to
ensure credit for all
involved!

10. …. Prior to this…
However, many of us who have actually conducted clinical
research, managed clinical studies and data collection and
analysis, and curated data sets have concerns about the
details. The first concern is that someone not involved in
the generation and collection of the data may not
understand the choices made in defining the parameters.
Special problems arise if data are to be combined from independent
studies and considered comparable. How heterogeneous were the
study populations? Were the eligibility criteria the same….
Actually a very fair
comment

11. See also http://reason.com/archives/2016/01/19/broken-science
This is really becoming
a hot topic right now

12. ……estimates for the reproducibility of preclinical research range
from 51 percent to 89 percent. They estimate that at least half of
all U.S. preclinical biomedical research funding—about $28 billion
annually—is therefore squandered……
http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165
But is this just an
“academic” problem,
or should industry
care?

13. http://www.cell.com/cell/abstract/S0092-8674%2809%2900316-X
Looks like a good
lead– certainly some
drug companies
thought so

15. http://f1000research.com/articles/5-136/v1
Another paper that
was a hot idea back in
the day…

16. http://www.nature.com/nrd/journal/v10/n9/full/nrd3439-c1.html
A first-of-a-kind analysis of Bayer's internal
efforts to validate 'new drug target' claims now
not only supports this view but suggests that
50% may be an underestimate; the company's
in-house experimental data do not match
literature claims in 65% of target-validation
projects, leading to project discontinuation.
Industry should
*really* care

20. http://www.cafepress.co.uk/+metadata+t-shirts
http://explorer.openphacts.org/
https://www.w3.org/TR/prov-o/

21. Quality?
http://lod-cloud.net/

22. Quality Of The Experiment
Independent Confirmations
Negative Assertions
Literature “dead ends”
Social Commentary / Sentiment Analysis
Marking Questionable Assertions

23. Reproducibility,
Meta Data &
Provenance

27. 2011 Here we talked about
how industry needs
open ontologies

28. 0
20
40
60
80
100
Year Six Months Three Months One Month
%OfOntologies
Updated In The Last…
Only 1/3rd updated in
the last year

29. OK so apples and
oranges, but still,
there’s a vast
difference between the
two

30. BAO incredibly
valuable resource,
need to support it!

31. We may never have
“enough” resource, so
what are the
alternatives to
ontology sustain?

32. https://www.qmarkets.org/live/pistoia/

33. ORPHANET Rare/Orphan
disease
Dynamic Phenotype
Network In RDF
Generated by TERMite
PhenotypeFinder
Ack. Michael Hughes
http://scibite.com
SciBite
Yellow = disease, pink
= on-the-fly
phenotype concept
generated by text
mining (not found in
HPO)

34. We need some cool
new ideas!

35. Reproducibility,
Meta Data &
Provenance
Ontology

37. 139755-83-2
A standard, but not
“open” – inhibits
certain projects

38. 21,000,000
structures
Open PHACTS built a
completely open
semantic chemistry
registry

39. http://www.slideshare.net/alasdair_gray/scientific-lenses-to-support-multiple-views-over-linked-chemistry-data
With which you can do
some very fancy things

40. http://www.inchi-trust.org/
Chemistry doesn’t
stand still

41. And we need to
support biologicals
too!

42. https://www.ebi.ac.uk/chembl/compound/inspect/CHEMBL1252
Excellent open-data
success story!

43. http://blogs.sciencemag.org/pipeline/archives/
2016/01/19/what-does-one-do-with-these
Some amazing
knowledge out there,
how do we get it “into
the graph”?

44. Reproducibility,
Meta Data &
Provenance
Ontology
Semantics 4
Therapeutics

46. The Biggest
Challenge In Data
Integration Is…
…The Data

47. DATA TOOLING
The best approach, not
doing either in
isolation but a dialog
between both

48. data tech
Much more time was
spent on issues with
the data than the tech.
Not saying the tech
was easy!

49. Dynamic Identifier Resolution
Mixing SPARQL & web services
Production Deployment
API Centric Integration
Business Question-Focus
API Management
“App Store” Ecosystem Semantic Chemistry
Nanopubs & Provenance Cool, Friendly UI’s

50. But, that every data
conversation
generated more and
more questions!

52. Chembl01
2009
When OPS Started

53. By the time we really
started making RDF!

54. And things didn’t
stand still!

65. Today- Chembl schema is
unrecognizable from 2009.
Open PHACTS is the way
companies can work with
providers to stay on top of
data evolution!

66. Reproducibility,
Meta Data &
Provenance
Ontology
Semantics 4
Therapeutics
Data
Evolution

68. *http://www.nature.com/news/genetics-a-gene-of-rare-effect-1.12773
PCSK9 is an example
of genomics,
informatics and drug
discovery coming
together for real
change

69. Researchers have found that
removing a gene called USF1
protects mice against heart
disease, diabetes and obesity
We don’t yet have a
system where we can
just jump and explore
the biology and
chemistry of the USF1
pathways….

70. Reproducibility,
Meta Data &
Provenance
Ontology
Semantics 4
Therapeutics
Data
Evolution
The Testable
Disease
Network
Conclusions - this is
what i’d be arguing
for in Open PHACTS
2016 