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Semantic eHealth:
Getting more out of biomedical data using Semantic Technology"
Joanne S. Luciano, PhD!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 [email protected]"The Shraga Segal Dept. of Microbiology, Immunology & Genetics"AND NIBN"Ben-Gurion University"Building 39, room -113"POB 653, Beer Sheva 84105, Israel"
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
Be’er Sheva, Israel
2
Instructor
Joanne S. Luciano Deputy Director
Web Science Research Center
Education
BS Boston University MS Boston University PhD Boston University Harvard Medical School (Post Doc)
Research Interests
Use and Develop Technology. Infrastructure and Analytics to Advance Science and Increase its Utility to Improve Health Outcomes
ApplicationAreas
Life Science & Healthcare Pathways, Influenza, Trans Med Semantic Technologies Web Ontology Languaege (OWL) Ontology Evaluation Medicine - Major Depressive Disorder Environmental Monitoring Supply Chain Financial
Email: [email protected]
3
Timeline"(earlier work: 10 years in Software Research & Development and Product Development)"
2009 1993
World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon SIG Mental Function and Dysfunction Sam Levin
Jackie Samson, Mc Lean Hospital Depression Research
1996
1995
2008 1994
Patents Sold to Advanced
Biological Laboratories
Belgium
Patents Offered at Ocean Tomo
Auction Chicago, IL
US Patent No. 6,317,73 Awarded
US Patents No. 6,063,028
Awarded
2001
2000
PhD
Thesis Proposal Approved
Workshop Neural Modeling of Cognitive and Brain Disorders
BioPAX
? Linked Data W3C HCLS BioDASH
EPOS
2006
EMPWR
Poster Presented ISMB 1997 PSB 1998
1997
2010
Rensselaer (RPI)
2011 2013
Health Web Science Book
U Pitt Greg Siegle
Collaboration
Yuezhang Xiao
Master’s Thesis (RPI)
Brendan Ashby Master’sThesis (RPI)
Center for!Multi-
disciplinary Research
and!Depression!Treatment!Selection!
!!
2014
I-Choose
4 Predictive Medicine, Inc. © 2010 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"
5 Predictive Medicine, Inc. © 2010 5
Translational Medicine"
• Rapid transformation of laboratory findings into clinically focused applications "
• ‘From bench to bedside and back’"• “and back” includes patients!"
6 Predictive Medicine, Inc. © 2010 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)"
7 Predictive Medicine, Inc. © 2010
Widespread"
8 Predictive Medicine, Inc. © 2010
Treatment Choice Vague No easy answer"
9 Predictive Medicine, Inc. © 2010 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"
10 10
Research Goals"
Illuminate recovery course
(personalized)
Properly diagnose and properly match patient with the best individualized treatment option available, including non-drug treatments
11 Predictive Medicine, Inc. © 2010 11
Today’s talk focuses on: Response to treatment
Treatment Response Study"
12 Predictive Medicine, Inc. © 2010 12
Depression Background"
• Clinical Depression"• Treatment"• Symptom Measurement"• No specific diagnosis"• No specific treatment"
13 Predictive Medicine, Inc. © 2010 13
Clinical Data"
Symptoms"" -HDRS (0-4 scale)"
"
Treatment"-Desipramine (DMI)"-Cognitive Behavioral Therapy (CBT)"
"
Outcome"" - Responders"
14 Predictive Medicine, Inc. © 2010 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.
15 Predictive Medicine, Inc. © 2010 15
Why Model?"
"
Easier to understand"Easier to manipulate"Easier to analyze"
Recasting the problem into mathematical terms makes it:
16 Predictive Medicine, Inc. © 2010
Neural Modeling of Depression
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.
Luciano Model
Workshop 1995 Book 1996
17 Predictive Medicine, Inc. © 2010 17
Understanding Recovery
18 Predictive Medicine, Inc. © 2010 18
Understanding Recovery"
19 Predictive Medicine, Inc. © 2010 19
Depression Data"
7 Symptom Factors ! !!"Physical:" "E Sleep " "" " "M, L Sleep " " " "" " "Energy " " " " ""Performance: "Work & Interests " " " ""Psychological: "Mood " " " " "" " "Cognitions " " " "" " "Anxiety " " ""
2 Treatments ! "Cognitive Behavioural Therapy (CBT)" "" " "Desipramine (DMI)"
"!Clinical Data " "Responders = improvement >= 50% on HDRS total
" " " "N = 6 patient each study" " "6 weeks " = 252 data points (converted to daily) "" " " each study (CBT and DMI)"
"
20 Predictive Medicine, Inc. © 2010 20
Overview Recovery Model and Parameters"
M
E W
MS
ES
A
C
21 Predictive Medicine, Inc. © 2010 21
Recovery Equation (Luciano Model)
"
+
++
- ==
22 Predictive Medicine, Inc. © 2010 22
Individual Patient Recovery Pattern and Error"
Example Patient (CBT)"
Fit of Model on for individual patient captures trends but not entire pattern. Not good enough."
23 Predictive Medicine, Inc. © 2010 23
Patient Group (CBT)"
Recovery Pattern and Error"
Model on data for patient treatment group captures entire pattern. Good fit of Model to data."
24 Predictive Medicine, Inc. © 2010 24
Latency"
25 Predictive Medicine, Inc. © 2010 25
Treatment Effects and Interaction Effects"
CBT Sequential
DMI (delayed)
CONCURRENT
DMI: • Interactions > 2x • Loops
26 Predictive Medicine, Inc. © 2010
Order and Time a symptom improves are both different "
Different Response Patterns "for Different Treatment"
CBT DMI CBT (talk: no drugs) DMI (drug: tricyclic antidepressant)"
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."
27 Predictive Medicine, Inc. © 2010 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."
28 Predictive Medicine, Inc. © 2010 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"
29 Predictive Medicine, Inc. © 2010 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"
30 Predictive Medicine, Inc. © 2010 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"
31
Data Driven Medicine:"
Data, Not Programs (Technology)
Sharing, Not Hoarding (Policy)
Individuals, Not Populations (Science)"
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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
33
Data, Not Programs
33 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
12
34 34
1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
Feet? Years? December? Noon? Dozen?
Data, Not Programs
34 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
12 Feet? Years? December? Noon? Dozen?
35
NHANES (Sample)"National Health and Nutrition Examination Survey
36
Data, Not Programs
Data Dictionaries: Without a data dictionary, a database management system [or any program] cannot access data from the database.”1
36 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
Better, But….
37
Data, Not Programs
Data Dictionaries: Without a data dictionary, a database management system [or any program] cannot access data from the database.”1
37 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
No!
Enable Reuse: Keep information
the data the data
38
Metadata (simplified)
38
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/biopax_release1.owl”/> <bp:smallMolecule rdf:ID=“#pyruvate” > <bp:SYNONYMS>pyroracemic acid</bp:SYNONYMS> <bp:SYNONYMS>2-oxo-propionic acid</bp:SYNONYMS> <bp:SYNONYMS>alpha-ketopropionic acid</bp:SYNONYMS> <bp:SYNONYMS>2-oxopropanoate</bp:SYNONYMS> <bp:SYNONYMS>2-oxopropanoic acid</bp:SYNONYMS> <bp:SYNONYMS>BTS</bp:SYNONYMS> <bp:SYNONYMS>pyruvic acid</bp:SYNONYMS> </bp:smallMolecule> </annotation> </species>
39
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
Metadata (Webified)
39
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Query results return
links to the original data!
Metadata (Webified) SADI Web Services
Adapted from Mark Wilkinson webscience20-120829124752-phpapp01
See: sadiframework.org Semantic Automated Discovery and Integration (SADI)
41
Data Sharing (Shafu)"
Predictive Medicine, Inc. © 2010
42 Predictive Medicine, Inc. © 2010 42
BioPathways Consortium BioPAX W3C Semantic Web for Health Care and Life Sciences (HCLSIG)
Establishing Communities of Interest/Practice
43
BioPAX -‐ Enabling Cellular Network Process Modeling
Metabolic Pathways
Molecular Interaction Networks
Signaling Pathways
Gene Regulatory Networks
Glycolysis Protein-Protein Apoptosis TFs in E. coli
Integrate these different conceptual models different implementations
Do it in stages… BioPAX Level 1, Level 2, Level 3, Level 4
44 44
BioPAX
Biological PAthway eXchange"
An abstract data model for biological pathway integration "
"Initiative arose from the community!
45 45
phosphoglucose isomerase 5.3.1.9
OWL (schema)
Instances (Individuals)
(data)
BioPAX Biochemical Reaction"
46 46
BioPAX Ontology"
Level 1 v1.0 (July 7th, 2004)
parts
how the parts are known to interact
a set of interactions
47
Before BioPAX With BioPAX
Common “computable semantic” enables scientific discovery
>200 DBs and tools
Database
Application
User
BioPAX - Simplify
48 Predictive Medicine, Inc. © 2010 48
Welcome and Thanks for listening.
You’re part of the World Wide Web Community. You’re level of involvement is whatever suits
you!
49
Thank You!
Lecturer, Department of Microbiology and
Immunology Faculty of Health Sciences
Email: [email protected]
Special thanks to Dozor Scholarship Award Ben-Gurion University of the Negev Ronit Temes Eitan Rubin
50
Joanne S. Luciano, BS, MS, PhD"Academic:!"[email protected]""Rensselaer Polytechnic Institute, Troy, NY"
University of California – Irvine, CA"Consulting:! [email protected]" Predictive Medicine, Inc., Belmont, MA"
Predictive Medicine, Inc. © 2010
Contact Info"