1. Semantic eHealth:
Getting more out of biomedical data using
Semantic Technology
"
Joanne S. Luciano,
The Shraga Segal Department of Microbiology, Immunology and Genetics
Seminar date Thursday, 26.12.13 at 14:15
Deichman Building (M8), 101
Ben-Gurion University
PhD!
Be’er Sheva, Israel
Dozor Visiting Scholar, Ben-Gurion University of the Negev 2013!
Rensselaer Polytechnic Institute, University of California, Irvine, USA"
"
Host: Dr. Eitan Rubin, Tel. 052-8897143 erubin@bgu.ac.il"
The Shraga Segal Dept. of Microbiology, Immunology & Genetics"
AND NIBN"
Ben-Gurion University"
Building 39, room -113"
POB 653, Beer Sheva 84105, Israel"
1

2. Instructor
Education
Research
Interests
Joanne S. Luciano
Deputy Director
Web Science Research Center
Email: jluciano@uci.edu
BS Boston University
MS Boston University
PhD Boston University
Harvard Medical School (Post Doc)
Use and Develop Technology.
Infrastructure and Analytics to Advance
Science and Increase its Utility to
Improve Health Outcomes
Life Science & Healthcare
Pathways, Influenza, Trans Med
Semantic Technologies
Web Ontology Languaege (OWL)
Application
Ontology Evaluation
Areas
Medicine - Major Depressive Disorder
Environmental Monitoring
Supply Chain
Financial
2

3. Timeline"
(earlier work: 10 years in Software Research & Development and Product Development)"
World Congress on
Neural Networks,
July 11-15, 1993,
Portland, Oregon SIG
Mental Function and
Dysfunction
Sam Levin
Patents Offered at
Ocean Tomo
Auction Chicago, IL
PhD
US Patents
No. 6,063,028
Awarded
BioPAX
Thesis Proposal
Approved
1995
1993
1994
2000
Linked Data
W3C HCLS
Poster Presented
BioDASH
ISMB 1997
EPOS
PSB 1998
US Patent No.
Workshop Neural Modeling of
6,317,73
Cognitive and Brain Disorders
Awarded
Jackie Samson,
Mc Lean Hospital
Depression
Research
Patents Sold
to Advanced
Biological
Laboratories
Belgium
Center for!
Multidisciplinary
Research
and!
Depression!
Treatment!
Selection!
!
!
Yuezhang
Xiao
Master’s
Thesis
(RPI)
2001 2006
1997
1996
EMPWR
U Pitt
Greg Siegle
Collaboration
2008 2009 2010 2011
2013
?
2014
I-Choose
Rensselaer
(RPI)
Health
Web Science Book
Brendan Ashby
Master’sThesis (RPI)
3

4. Overview"
Introduction "
Depression Research"
"How did a nice girl like me,"
"
"wind up in a field like this?"
Changing Times & What they mean for"
"Science, Technology, and Policy"
Tools, Standards, Web Scale"
Predictive Medicine, Inc. © 2010
4
4

5. Translational Medicine"
• Rapid transformation of laboratory findings into
clinically focused applications "
• ‘From bench to bedside and back’"
• “and back” includes patients!"
Predictive Medicine, Inc. © 2010
5
5

6. HUGE PROBLEM"
Characterized by persistent and pathological sadness,
dejection, and melancholy"
Prevalence (US)"
"6% year (18 million)"
"16% experience it in their lifetime"
Cost "
"44 Billion (1990)"
Impact"
"1% Improvement means (180, 000 people helped)"
"1% Improvement means (440 million in savings)"
Predictive Medicine, Inc. © 2010
6
6

7. Widespread"
Predictive Medicine, Inc. © 2010
7

8. Treatment Choice Vague
No easy answer"
Predictive Medicine, Inc. © 2010
8

9. Overview"
• Why we did this work - to improve quality of life for millions
of people suffering from depression"
• How we did it - used differential equations
(“neural network”) to model and compare
response to different antidepressant
treatments"
• What we found - different response patterns for the two
treatments - the order and timing of improvement of
symptoms were different"
• What we think it means - improvement in selection of
treatment thereby reducing unnecessary costs and
suffering. Potentially saving lives"
Predictive Medicine, Inc. © 2010
9
9

10. Research Goals"
Properly diagnose and properly
match patient with the best individualized
treatment option available, including
non-drug treatments
Illuminate recovery
course
(personalized)
10
10

11. Treatment Response Study"
Today’s
talk
focuses
on:
Response
to
treatment
Predictive Medicine, Inc. © 2010
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11

12. Depression Background"
•
•
•
•
•
Clinical Depression"
Treatment"
Symptom Measurement"
No specific diagnosis"
No specific treatment"
Predictive Medicine, Inc. © 2010
12
12

13. Clinical Data"
Symptoms"
" -HDRS (0-4 scale)"
"
Treatment"
-Desipramine (DMI)"
-Cognitive Behavioral Therapy (CBT)"
"
Outcome"
" - Responders"
Predictive Medicine, Inc. © 2010
13
13

14. Hamilton
Psychiatric Scale for Depression"
Clinical Instrument standard measure in clinical trials. "
Example of first three items of 21 items that measure individual"
Symptom intensity.
Predictive Medicine, Inc. © 2010
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14

15. Why Model?"
Recasting
the
problem
into
mathematical
terms
makes
it:
"
Easier to understand"
Easier to manipulate"
Easier to analyze"
Predictive Medicine, Inc. © 2010
15
15

16. Workshop 1995
Book 1996
Neural
Modeling
of
Depression
Luciano Model
1996 Luciano, J., Cohen, M. Samson, J. ”Neural Network
Modeling of Unipolar Depression,” Neural Modeling of
Cognitive and Brain Disorders, World Scientific Publishing
Company, eds. J. Reggia and E. Ruppin and R. Berndt. Book
cover; chapter pp 469-483.
Predictive Medicine, Inc. © 2010
16

17. Understanding Recovery
Predictive Medicine, Inc. © 2010
17
17

18. Understanding Recovery"
Predictive Medicine, Inc. © 2010
18
18

19. Depression Data"
7 Symptom Factors
!
"Physical:"
"
"
"
"
"Performance:
"Psychological:
"
"
"
"
2 Treatments
"
!!
"E Sleep
"M, L Sleep
"Energy "
"Work & Interests
"Mood "
"Cognitions
"Anxiety "
"
"
"
"
"
"
"
"
"
"
"
"
!
"
"Cognitive Behavioural Therapy (CBT)"
"Desipramine (DMI)"
"
"
"
"Responders = improvement >= 50% on HDRS total
"
"N = 6 patient each study"
"6 weeks "
= 252 data points (converted to daily) "
"
"
each study (CBT and DMI)"
"
"
"
"
"
"
"
"
"
"
"
""
"
!
Clinical Data
"
"
"
Predictive
"
Medicine,
Inc. © 2010
19
19

20. Overview
Recovery Model and Parameters"
W
A
C
M
Predictive Medicine, Inc. © 2010
E
ES
MS
20
20

21. Recovery Equation
(Luciano Model)
"
= +
+
+
Predictive Medicine, Inc. © 2010
21
21

22. Example Patient (CBT)"
Individual Patient Recovery Pattern and Error"
Fit of Model on for individual patient captures trends but
22
not entire pattern. Not good enough."
Predictive Medicine, Inc. © 2010
22

23. Patient Group (CBT)"
Recovery Pattern and Error"
Model on data for patient treatment group captures
23
entire pattern. Good
Predictive Medicine, Inc. © 2010fit of Model to data."
23

24. Latency"
Predictive Medicine, Inc. © 2010
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24

25. Treatment Effects
and Interaction Effects"
CBT
Sequential
DMI:
• Interactions > 2x
• Loops
Predictive Medicine, Inc. © 2010
DMI
(delayed)
CONCURRENT
25
25

26. Different Response Patterns "
for Different Treatment"
Order and Time a
symptom improves
are both different
"
This is important because it shows
how an antidepressant medication
could lead to a suicide.
By giving a suicidal patient DMI, you
could increase the patients energy
before the suicidal thoughts improve.
This could give them the energy to
act on those suicidal thoughts."
DMI
CBT
Predictive Medicine, Inc. © 2010
CBT (talk: no drugs)
DMI (drug: tricyclic antidepressant)"
26

27. Conclusion (Depression)"
• Why we did this work - to improve quality of life for millions
of people suffering from depression"
• How we did it - used differential equations (“neural network”)
to model and compare response to different antidepressant
treatments"
• What we found - different response patterns for the two
treatments - the order and timing of improvement of
symptoms were different"
• What we think it means - improvement
in selection of treatment thereby
reducing unnecessary costs and
suffering. Potentially saving lives."
Predictive Medicine, Inc. © 2010
27
27

28. Overview"
Introduction "
Depression Research"
"How did a nice girl like me,"
"
"wind up in a field like this?"
Changing Times & What they mean for"
"Science, Technology, and Policy"
Tools, Standards, Web Scale"
Predictive Medicine, Inc. © 2010
28
28

29. Overview"
Introduction "
Depression Research"
"How did a nice girl like me,"
"
"wind up in a field like this?"
Changing Times & What they mean for"
"Science, Technology, and Policy"
Tools, Standards, Web Scale"
Predictive Medicine, Inc. © 2010
29
29

30. Part 2, Changing Times"
1. Intro to Data Science"
Shifts (programs to data, populations to individuals, hoarding to sharing)"
What makes data useful?"
Can we exploit the web to access data?"
2. Tools to Integrate Biomedical Data"
By Hand "
Using Tools "
Automated Integration and Using the Web to Compute (SADI Services)"
3. Knowledge Standards and Best Practices that
enable web scale Integration"
Connecting data"
5 Stars"
5 Stars not enough"
Predictive Medicine, Inc. © 2010
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30

31. Data Driven Medicine:
"
Data, Not Programs
(Technology)
Sharing, Not Hoarding
(Policy)
Individuals, Not Populations
(Science)
"
31
31

32. Data Science?
"
How do work with data?"
How do you treat your data?"
"
How easy is it for you to use data?"
Yours? Someone else’s?"
"
What makes data easy or hard to reuse?"
What if anything can be done about it?"
Predictive Medicine, Inc. © 2010
32

33. Data, Not Programs
12
33
1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
33

34. Data, Not Programs
12
Feet?
Feet?
Years?
Years?
December?
December?
Noon?
Noon?
Dozen?
Dozen?
34
1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.34
34
1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.

35. NHANES (Sample)
"
National Health and Nutrition Examination Survey
35

36. Data, Not Programs
Data Dictionaries:
Without a data
dictionary, a
database
management
system [or any
program] cannot
access data from
the database.”1
Better,
But….
36
1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
36

37. Data, Not Programs
Data Dictionaries:
Enable Reuse:
Keep information
the data
the data
Without a data
dictionary, a
database
management
system [or any
program] cannot
access data from
the database.”1
No!
37
1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
37

38. Metadata (simplified)
Biochemical Reaction
<reaction
id=“pyruvate_dehydrogenase_rxn”/>
<listOfReactants>
<speciesRef species=“NADP+”/>
<speciesRef species=“CoA”/>
<speciesRef species=“pyruvate”/>
</listOfReactants>
<listOfProducts>
<speciesRef species=“NADPH”/>
<speciesRef species=“acetyl-CoA”/>
<speciesRef species=“CO2”/>
</listOfProducts>
<listOfModifers>
<modifierSpeciesRef
species=“pyruvate_dehydrogenase_E1”/
>
</listOfModifiers>
</reaction>
Synonyms
<species id=“pyruvate” metaid=“pyruvate”>
<annotation xmlns:bp=“http://biopax.org/release1/bio
<bp:smallMolecule rdf:ID=“#pyruvate” >
<bp:SYNONYMS>pyroracemic acid</bp:SYNONYMS
<bp:SYNONYMS>2-oxo-propionic acid</bp:SYNONY
<bp:SYNONYMS>alpha-ketopropionic acid</bp:SYN
<bp:SYNONYMS>2-oxopropanoate</bp:SYNONYMS
<bp:SYNONYMS>2-oxopropanoic acid</bp:SYNONY
<bp:SYNONYMS>BTS</bp:SYNONYMS>
<bp:SYNONYMS>pyruvic acid</bp:SYNONYMS>
</bp:smallMolecule>
</annotation>
</species>
38
38

39. Metadata (Webified)
Instead of textual labels
<bp:smallMolecule rdf:ID= #pyruvate >
<bp:Xref>
<bp:unificationXref rdf:ID= #unificationXref119">
<bp:DB>LIGAND</bp:DB>
<bp:ID>c00022</bp:ID>
</bp:unificationXref>
</bp:Xref>
</bp:smallMolecule>
Use actual URIs
39
39

40. Metadata (Webified)
SADI Web Services
Query results
return
links to the original
data!
See: sadiframework.org
Semantic
Automated
Discovery
and
Integration
(SADI)
Adapted from Mark Wilkinson webscience20-120829124752-phpapp01
40

41. Data Sharing (Shafu)
"
Predictive Medicine, Inc. © 2010
41

42. Establishing
Communities
of
Interest/Practice
BioPathways
Consortium
BioPAX
W3C
Semantic
Web
for
Health
Care
and
Life
Sciences
(HCLSIG)
Predictive Medicine, Inc. © 2010
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42

43. BioPAX
-­‐
Enabling
Cellular
Network
Process
Modeling
Glycolysis
Metabolic
Pathways
Protein-Protein
Molecular
Interaction
Networks
Apoptosis
Signaling
Pathways
TFs in E. coli
Gene Regulatory
Networks
Integrate these
different conceptual models
different implementations
Do it in stages… BioPAX Level 1, Level 2, Level 3, Level 4
43

44. BioPAX
Biological PAthway eXchange
"
An abstract data model for biological
pathway integration"
"
Initiative arose from the community
!
44
44

45. BioPAX Biochemical Reaction
"
OWL
(schema)
Instances
(Individuals)
(data)
phosphoglucose
isomerase
5.3.1.9
45
45

46. BioPAX Ontology
"
a set of
interactions
parts
how the parts are known to
interact
Level 1 v1.0 (July 7th, 2004)
46
46

47. BioPAX - Simplify
>200 DBs and tools
Application
Database
User
Before BioPAX
With BioPAX
Common “computable semantic” enables scientific
discovery
47

48. Welcome
and
Thanks
for
listening.
You’re
part
of
the
World
Wide
Web
Community.
You’re
level
of
involvement
is
whatever
suits
you!
Predictive Medicine, Inc. © 2010
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48

49. Thank You!
Special thanks to
Dozor Scholarship Award
Ben-Gurion University of the
Negev
Ronit Temes
Lecturer,
Department of Microbiology and
Immunology
Faculty of Health Sciences
Eitan Rubin
Email: erubin@bgu.ac.il
49

50. Contact Info"
Joanne S. Luciano, BS, MS, PhD"
Academic:!
"j.luciano@uci.edu"
"Rensselaer Polytechnic Institute, Troy, NY"
University of California – Irvine, CA"
Consulting:!
jluciano@predmed.com"
Predictive Medicine, Inc., Belmont, MA"
Predictive Medicine, Inc. © 2010
50