2013 dec bgu_israel_luciano_dec_22
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Semantic eHealth: Getting more out of biomedical data using
Semantic TechnologyInstructors:
Joanne S. Luciano, PhDRensselaer Polytechnic Institute, University of California, Irvine, USA
Eitan Rubin, PhDBen-Gurion University
December 22-25, 2013
Ben-Gurion University of the Negev, Israel
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Instructor
Lecturer,
Department of Microbiology and Immunology
Faculty of Health Sciences
InterestsUnderstand the role genetics plays in the development of diseases
Research
Novel methods for disease stratification using genetic analysis as predictors of treatment outcomes.
Improved methods for computational target prioritization in genetic association studies
An end-user programming language for biologists
Email: [email protected]
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Instructor
Joanne S. LucianoDeputy Director
Web Science Research Center
Interests
Use and Develop Technology. Infrastructure and Analytics to Advance Science and Increase its Utility to Improve Health Outcomes
Research
BioPAX, TMO, InfluenzO
General Framework for Ontology Evaluation
Systems Biology and Medicine - Major Depressive Disorder (MDD)
Medicine, Health, WellbeingEmail: [email protected]
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Timeline(earlier work: 10 years in Software Research & Development and Product Development)
20091993
World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon SIGMental Function andDysfunctionSam Levin
Jackie Samson,Mc Lean HospitalDepression Research
1996
1995
20081994
Patents Soldto Advanced
Biological Laboratories
Belgium
Patents Offered at
Ocean Tomo Auction
Chicago, IL
US Patent No. 6,317,73
Awarded
US PatentsNo.
6,063,028Awarded
2001
2000
PhD
Thesis Proposal Approved
Workshop Neural Modeling of Cognitive and Brain Disorders
BioPAX
?Linked DataW3C HCLSBioDASH
EPOS
2006
EMPWR
Poster Presented ISMB 1997PSB 1998
1997
2010
Rensselaer(RPI)
20112012
2013
U PittGreg Siegle
Collaboration
YuezhangXiao
Master’sThesis(RPI)
Brendan AshbyMaster’sThesis
(RPI)
Center forMulti-
disciplinary Research
andDepressionTreatmentSelection
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Overview
Promises:
0. Introduction – Depression ResearchHow did a nice girl like me,
wind up in a field like this?
1.Intro to Data Science
2.Tools to Integrate Biomedical Data
3.Knowledge Standards and Best Practices that enable web scale Integration
6Predictive Medicine, Inc. © 20106
BioPathways Consortium
BioPAX
W3C Semantic Web for Health Care and Life Sciences (HCLSIG)
Establishing Communities of Interest/Practice
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BioPAX - Enabling Cellular Network Process Modeling
MetabolicPathways
MolecularInteractionNetworks
SignalingPathways
Gene RegulatoryNetworks
Glycolysis Protein-Protein Apoptosis TFs in E. coli
8Predictive Medicine, Inc. © 20108
Translational Medicine
• Rapid transformation of laboratory findings into clinically focused applications
• ‘From bench to bedside and back’• “and back” includes patients!
9Predictive Medicine, Inc. © 20109
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)
10Predictive Medicine, Inc. © 2010
Widespread
11Predictive Medicine, Inc. © 2010
Treatment Choice VagueNo easy answer
12Predictive Medicine, Inc. © 201012
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
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Research Goals
Illuminate recovery course
(personalized)
Properly diagnose and properlymatch patient with the best individualized
treatment option available, includingnon-drug treatments
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Today’s talk focuses on:Response to treatment
Treatment Response Study
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Depression Background
• Clinical Depression
• Treatment
• Symptom Measurement
• No specific diagnosis
• No specific treatment
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Clinical Data
Symptoms
-HDRS (0-4 scale)
Treatment-Desipramine (DMI)
-Cognitive Behavioral Therapy (CBT)
Outcome
- Responders
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Hamilton Psychiatric Scale for Depression
Clinical Instrument standard measure in clinical trials. Example of first three items of 21 items that measure individualSymptom intensity.
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Why Model?
Easier to understandEasier to manipulateEasier to analyze
Recasting the problem into mathematical termsmakes it:
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Understanding Recovery
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Understanding Recovery
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Depression Data
7 Symptom Factors
Physical: E Sleep M, L Sleep
EnergyPerformance: Work & InterestsPsychological: Mood
CognitionsAnxiety
2 Treatments Cognitive Behavioural Therapy (CBT)Desipramine (DMI)
Clinical Data Responders = improvement >= 50% on HDRS totalN = 6 patient each study
6 weeks = 252 data points (converted to daily)
each study (CBT and DMI)
22Predictive Medicine, Inc. © 201022
Overview Recovery Model and Parameters
M
EW
MS
ES
A
C
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Recovery Equation(Luciano Model)
+
+
+
-==
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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.
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Patient Group (CBT)
Recovery Pattern and Error
Model on data for patient treatment group captures entire pattern. Good fit of Model to data.
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Latency
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Treatment Effects and Interaction Effects
CBTSequential
DMI(delayed)
CONCURRENT
DMI: •Interactions > 2x •Loops
28Predictive Medicine, Inc. © 2010
Order and Time a symptom improves are both different
Different Response Patterns for Different Treatment
CBT DMICBT (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.
29Predictive Medicine, Inc. © 201029
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.
30Predictive Medicine, Inc. © 201030
Give me a break!!!Give me a break!!!
One more slide One more slide (so you see what’s coming (so you see what’s coming
whenwhenwe return)we return)
31Predictive Medicine, Inc. © 201031
Inside the Overview
1. Intro to Data ScienceShifts (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 DataBy Hand
Using Tools
Automated
3. Knowledge Standards and Best Practices that enable web scale Integration
Connecting data
5 Stars
5 Stars not enough
32Predictive Medicine, Inc. © 201032
Give me a break!!!Give me a break!!!
33Predictive Medicine, Inc. © 201033
Inside the Overview
1. Intro to Data ScienceShifts (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 DataBy Hand
Using Tools
Automated
3. Knowledge Standards and Best Practices that enable web scale Integration
Connecting data
5 Stars
5 Stars not enough
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Intro to Data Science
What do you think data is?
What could data science possibly mean?
Can data be reused once the original purpose (study) is done?
Predictive Medicine, Inc. © 2010
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Data, Not Programs
351. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
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1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
Feet?Years?December?Noon?Dozen?
Data, Not Programs
361. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
12Feet?Years?December?Noon?Dozen?
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Data, Not Programs
371. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
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Data, Not Programs
Data Dictionaries:
Without a data dictionary, a database management system [or any program] cannot access data from the database.”1
381. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
Duh!
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Data, Not Programs
Data Dictionaries:
Without a data dictionary, a database management system [or any program] cannot access data from the database.”1
391. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
Duh!
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Metadata (simplified)
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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>
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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)
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Query results return
links to the original data!
Metadata (Webified)
Adapted from Mark Wilkinson webscience20-120829124752-phpapp01
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Data Sharing (Shafu)
Predictive Medicine, Inc. © 2010
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Had enough for now?Had enough for now?
Ready to start getting your Ready to start getting your hands dirty?hands dirty?
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Joanne S. Luciano, BS, MS, PhDAcademic:
Rensselaer Polytechnic Institute, Troy, NY
University of California – Irvine, CA
Consulting:
Predictive Medicine, Inc., Belmont, MA
Predictive Medicine, Inc. © 2010
CV Background slides...
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Whew!Whew!
Now that was fun, wasn’t Now that was fun, wasn’t it?it?
Any questions?Any questions?
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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 highlighted on book cover
Workshop 1995Book 1996
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Inside the Overview
1. Tools to Integrate Biomedical Data• By Hand
• Really by hand, i.e. depression research
• Cutting and pasting between text editors, spreadsheets, and command lines
• Using Tools • KNIME
• Automated • Protégé
• Gruff & Allegrograph
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Diabetes Classification
WHO Recommendation 2011
HbA1c 48 mmol/mol (6.5%) cut point• stringent quality assurance tests
• assays are standardised to international reference values,
• no conditions present which preclude its accurate measurement.
A value of less than 48 mmol/mol (6.5%) does not exclude diabetes diagnosed using glucose tests.
Predictive Medicine, Inc. © 2010
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Diabetes Classification
Situations where HbA1c is not appropriate for diagnosis of diabetes:
• ALL children and young people
• Patients of any age suspected of having Type 1 diabetes
• Patients with symptoms of diabetes for less than 2 months
• Patients at high diabetes risk who are acutely ill (e.g. those requiring hospital admission)
• Patients taking medication that may cause rapid glucose rise e.g. steroids, antipsychotics
• Patients with acute pancreatic damage, including pancreatic surgery
• In pregnancy
• Presence of genetic, haematologic and illness-related factors that influence HbA1c and its measurement - see Annex 1 from WHO report
Predictive Medicine, Inc. © 2010