2013 dec 26_bgu_israel_seminar_l_luciano

1

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"


Translational Medicine"

•  Rapid transformation of laboratory findings into clinically focused applications "

•  ‘From bench to bedside and back’"•  “and back” includes patients!"


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"


Treatment Choice Vague No easy answer"


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


Today’s talk focuses on: Response to treatment

Treatment Response Study"


Depression Background"

•  Clinical Depression"•  Treatment"•  Symptom Measurement"•  No specific diagnosis"•  No specific treatment"


Clinical Data"

Symptoms"" -HDRS (0-4 scale)"

"

Treatment"-Desipramine (DMI)"-Cognitive Behavioral Therapy (CBT)"

"

Outcome"" - Responders"


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.


Why Model?"

"

Easier to understand"Easier to manipulate"Easier to analyze"

Recasting the problem into mathematical terms makes it:


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


Understanding Recovery


Understanding Recovery"


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)"

"


Overview Recovery Model and Parameters"

M

E W

MS

ES

A

C


Recovery Equation (Luciano Model)

"

+

++

- ==


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."


Patient Group (CBT)"

Recovery Pattern and Error"

Model on data for patient treatment group captures entire pattern. Good fit of Model to data."


Latency"


Treatment Effects and Interaction Effects"

CBT Sequential

DMI (delayed)

CONCURRENT

DMI: •  Interactions > 2x •  Loops


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."


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."


Overview"






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)"

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

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


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


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


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

40

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)"



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


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"


Contact Info"

2013 dec 26_bgu_israel_seminar_l_luciano

Health & Medicine

recoverypredictive medicine

analyzepredictive medicine

widespreadpredictive

livespredictive medicine

savings predictive medicine

web scalepredictive

depression data

easy answerpredictive