2013 dec bgu_israel_luciano_dec_22

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  • Semantic eHealth: getting more out of biomedical data using Semantic Technology
    Short Course — Offered 22-25 December 2013
    http://tw.rpi.edu/web/event/SemanticEHealth
    Joanne S. Luciano, PhD
    Rensselaer Polytechnic Institute
    Eitan Rubin, PhD
    FOHS, Ben-Gurion University of the Negev
    Description
    In this course we will introduce a set of advanced tools that can be used to integrate bio-medical data and use it to answer clinical questions. The course introduces the new field of data science, with an emphasis on how it relates to biomedical research. It provides the knowledge of the standards and best practices that enable integration across the web and data mining at web scale.
    Students will learn how to build computer-based applications that can automatically integrate bio-medical data and how they can be used to ask and answer questions. Using datasets that can be found freely on the web or data generated in the lab, we will show how to convert them to formats that enable easy integration, and how to use semantic technology to describe how the data are related to enable automatic integration and visualization of the data.
    In addition, we will (1) introduce the CRISP-DM process of knowledge mining and the Semantic Web Development Methodology; (2) explain the problems of data integration from three aspects, i.e. technically, ontologically, and domain specific, (3) we will demonstrate how each of these data integration problems can be approached; and (4) we will help student realize how to utilize knowledge mining in their own research.
    Credit
    Graduate students will receive 1 credit point for the course. Grading will be on a pass/fail basis only.
    Course Outline (preliminary!)
    Sunday, June 14
    22/12/2013, 11:15-14:00, Building M8, room 08
    23/12/2013, 14:15-17:00, Building M8, room 08
    25/12/2013, 09:15-12:00, Building M8, room 002
    (Three more 2-h lectures will be announced in the coming weeks)
    Prerequisites
    Prior knowledge of programming is NOT required.
    Intended Audience
    The course should be appropriate for M.Sc student and above.
    To register and for additional details, please contact Eitan Rubin
    IF YOU ARE REGISTERING, PLEASE USE THE FOLLOWING AT THE TITLE OF YOUR EMAIL: Registration -Semantic eHealth
  • ---
  • I became aware of the difficulties of conducting multidisciplinary research. In particular, its huge negative impact on health care.
    At that time, it was easy to recognize how data in the form of images, such as PET scans and fMRIs contributed to our knowledge and understanding, but it was not so obvious how data and modeling also contributed the same, if not more.
    At the Tetherless world and in my role as the Deputy Director of the Web Science Research Center, I continue to explore these issues and to hopefully discover solutions.
    ---
  • BioPathways played a leading role in the creation of BioPAX-
    BioPAX first sem web application in life scinces which formed part of basis for the arguremt to create a hcls group.
    BioPathways Consortium
    Co-organize
    BioPAX
    Co-founded – Obtained initial funding from DoE, and 2nd year to fund workshop
    W3C
    Semantic Web for Health Care and Life Sciences
    Helped create
    BioRDF – move bio-clinical data to RDF / DEMO
    Organization of workshops, Journal special issue
    BioDASH Demo – Highlighted by TBL BioIT May 2005
    Siderean Demo – Highlighted by TBL –ISWC Oct 2005
    OWL-WG, Infectious Disease Ontology
    Participate – lead collaboration on Influezna Ontology to support research and surveillance
  • BioPAX
    Pathway means different things to different people
    Signaling pathway
    Metabolic pathway
    Gene regulatory pathway
    An ontology to support integration and exchange of biological pathway data of different types (and formats)
  • bridge the gap between basic and clinical sciences, to ensure that basic research discoveries of potential relevance to patient care are effectively applied
    Translational medicine aims broadly at the rapid transformation of laboratory findings into clinically focused applications – ‘from bench to bedside and back’. There has long been a consensus that there is a pressing need to bridge the gap between basic and clinical sciences, to ensure that basic research discoveries of potential relevance to patient care are effectively applied. This is a formidable challenge to implement and some of the key problems stem from the lack of appropriate frameworks and models that link clinically relevant information (in particular that related to multi-scale pathways and networks) to the knowledge obtained across multiple disciplines, experimental platforms and biological systems.
    Bedside graphic: http://www.blogthecoast.com/rainbow/3980%20hospital%20room.jpg
  • Mental Disorders in America from: http://www.nimh.nih.gov/health/publications/the-numbers-count-mental-disorders-in-america/index.shtml
    Mental disorders are common in the United States and internationally. An estimated 26.2 percent of Americans ages 18 and older — about one in four adults — suffer from a diagnosable mental disorder in a given year.1 When applied to the 2004 U.S. Census residential population estimate for ages 18 and older, this figure translates to 57.7 million people.2Even though mental disorders are widespread in the population, the main burden of illness is concentrated in a much smaller proportion — about 6 percent, or 1 in 17 — who suffer from a serious mental illness.1 In addition, mental disorders are the leading cause of disability in the U.S. and Canada for ages 15-44.3 Many people suffer from more than one mental disorder at a given time. Nearly half (45 percent) of those with any mental disorder meet criteria for 2 or more disorders, with severity strongly related to comorbidity.1
    In the U.S., mental disorders are diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV).4
    Mood Disorders
    Mood disorders include major depressive disorder, dysthymic disorder, and bipolar disorder.
    * Approximately 20.9 million American adults, or about 9.5 percent of the U.S. population age 18 and older in a given year, have a mood disorder.1
    * The median age of onset for mood disorders is 30 years.5
    * Depressive disorders often co-occur with anxiety disorders and substance abuse.5
    Major Depressive Disorder
    * Major Depressive Disorder is the leading cause of disability in the U.S. for ages 15-44.3
    * Major depressive disorder affects approximately 14.8 million American adults, or about 6.7 percent of the U.S. population age 18 and older in a given year.1
    * While major depressive disorder can develop at any age, the median age at onset is 32.5
    * Major depressive disorder is more prevalent in women than in men.6
    References
    1. Kessler RC, Chiu WT, Demler O, Walters EE. Prevalence, severity, and comorbidity of twelve-month DSM-IV disorders in the National Comorbidity Survey Replication (NCS-R). Archives of General Psychiatry, 2005 Jun;62(6):617-27.
    2. U.S. Census Bureau Population Estimates by Demographic Characteristics. Table 2: Annual Estimates of the Population by Selected Age Groups and Sex for the United States: April 1, 2000 to July 1, 2004 (NC-EST2004-02) Source: Population Division, U.S. Census Bureau Release Date: June 9, 2005. http://www.census.gov/popest/national/asrh/
    3. The World Health Organization. The World Health Report 2004: Changing History, Annex Table 3: Burden of disease in DALYs by cause, sex, and mortality stratum in WHO regions, estimates for 2002. Geneva: WHO, 2004.
    4. American Psychiatric Association. Diagnostic and Statistical Manual on Mental Disorders, fourth edition (DSM-IV). Washington, DC: American Psychiatric Press, 1994.
    5. Kessler RC, Berglund PA, Demler O, Jin R, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication (NCS-R). Archives of General Psychiatry. 2005 Jun;62(6):593-602.
    6. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association, 2003; Jun 18;289(23):3095-105.
  • Neural networks are modeling tools that can help us understand
    processes. They are used to identify patterns and to understand how
    individual patterns are generated. We use neural networks of ordinary
    differential equations to help us understand the effects of treatment
    on the pattern of recovery. First I will tell you about the how we
    constructed our neural network model architecture. Then I will show
    you how differential equations give us greater ability in the study
    depression recovery. A differential equation is a way of applying
    algebra and calculus to describe the relationships between the rates
    of change of the quantaties we're interested in.
  • Skipping over the trials and tribulations of identifying an appropriate problem, I will first say that having come from industry I was aware of customer needs. In this context it meant that the research questions, guidance and judgment about whether we were being useful would come from the clinicians, not from the theorists.
    The far reaching goal of the research aims at individual treatment. What is now called personalized medicine. This is because not every treatment works on every person. Because treatment choices are made by trial end error, the second goal aims to identify the correct treatment for a given individual. The third goal aims to get to the first two through a better understanding the underlying dynamics of depression. It focuses on the course of the recovery process because the research is grounded in clinical data and there are no data on depression before the diagnosis of depression is made. So outcome and recovery data were all we had to work with.
  • Two approaches were undertaken, one that took advantage of baseline and outcome data ant the other that took advantage of the limited time course (repeated measure) data available through the course of a clinical trial.
    This was the first time these advanced methods were applied to clinical data.
  • We did this because depression a big problem, lots suffer, sometimes fatal and is number one cost to business
    Currently up to practioner - not objective - not based on individual symptomatology -- one size fits all, can do better.
  • Before my PhD Defense, this work was recognized at an NIH sponsored workshop in 1996. I was honored, when the book came out several months later to find that the Editors had chosen to place my model on the cover of the book. I was also pleased that the placed it over an fMRI image because in fact, my model included hypotheses linking clinical symptoms and brain region activity.
  • http://www.ncbi.nlm.nih.gov/pubmed/21722564
  • http://www.ncbi.nlm.nih.gov/pubmed/21722564
  • 2013 dec bgu_israel_luciano_dec_22

    1. 1. Semantic eHealth: Getting more out of biomedical data using Semantic Technology Instructors: Joanne S. Luciano, PhD Rensselaer Polytechnic Institute, University of California, Irvine, USA Eitan Rubin, PhD Ben-Gurion University December 22-25, 2013 Ben-Gurion University of the Negev, Israel 1
    2. 2. Instructor Interests Understand the role genetics plays in the development of diseases Novel methods for disease stratification using genetic analysis as predictors of treatment outcomes. Research Improved methods for computational target prioritization in genetic association studies Lecturer, Department of Microbiology and Immunology Faculty of Health Sciences An end-user programming language for biologists Email: erubin@bgu.ac.il 2
    3. 3. Instructor Interests Use and Develop Technology. Infrastructure and Analytics to Advance Science and Increase its Utility to Improve Health Outcomes BioPAX, TMO, InfluenzO Research Joanne S. Luciano Deputy Director Web Science Research Center Email: jluciano@uci.edu General Framework for Ontology Evaluation Systems Biology and Medicine Major Depressive Disorder (MDD) Medicine, Health, Wellbeing 3
    4. 4. 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 Thesis Proposal Approved 1995 PhD US Patents No. 6,063,028 Awarded Patents Offered at Ocean Tomo Auction Chicago, IL BioPAX EMPWR 1997 U Pitt Greg Siegle Collaboration Patents Sold to Advanced Biological Laboratories Belgium Center for Multidisciplinary Yuezhang Research Xiao and Master’s Depression Thesis (RPI) Treatment Selection 2001 2006 2008 2009 2010 2011 2012 1996 1993 1994 2000 Jackie Samson, Linked Data Mc Lean W3C HCLS Poster 2013 Hospital Brendan Ashby BioDASH Presented Depression Rensselaer Master’sThesis EPOS ISMB 1997 Research (RPI) (RPI) PSB Workshop Neural Modeling 1998 US Patent No. 6,317,73 of Cognitive and Brain Awarded Disorders 4 ?
    5. 5. Overview Promises: 0. Introduction – Depression Research How 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 Predictive Medicine, Inc. © 2010 5 5
    6. 6. Establishing Communities of Interest/Practice BioPathways Consortium BioPAX W3C Semantic Web for Health Care and Life Sciences (HCLSIG) Predictive Medicine, Inc. © 2010 6 6
    7. 7. BioPAX - Enabling Cellular Network Process Modeling Glycolysis Metabolic Pathways Protein-Protein Molecular Interaction Networks Apoptosis Signaling Pathways TFs in E. coli Gene Regulatory Networks 7
    8. 8. 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 8 8
    9. 9. 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 9 9
    10. 10. Widespread Predictive Medicine, Inc. © 2010 10
    11. 11. Treatment Choice Vague No easy answer Predictive Medicine, Inc. © 2010 11
    12. 12. 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 12 12
    13. 13. Research Goals Properly diagnose and properly match patient with the best individualized treatment option available, including non-drug treatments Illuminate recovery course (personalized) 13 13
    14. 14. Treatment Response Study Today’s talk focuses on: Response to treatment Predictive Medicine, Inc. © 2010 14 14
    15. 15. Depression Background • • • • • Clinical Depression Treatment Symptom Measurement No specific diagnosis No specific treatment Predictive Medicine, Inc. © 2010 15 15
    16. 16. Clinical Data Symptoms -HDRS (0-4 scale) Treatment -Desipramine (DMI) -Cognitive Behavioral Therapy (CBT) Outcome - Responders Predictive Medicine, Inc. © 2010 16 16
    17. 17. 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 17 17
    18. 18. Why Model? Recasting the problem into mathematical terms makes it: Easier to understand Easier to manipulate Easier to analyze Predictive Medicine, Inc. © 2010 18 18
    19. 19. Understanding Recovery Predictive Medicine, Inc. © 2010 19 19
    20. 20. Understanding Recovery Predictive Medicine, Inc. © 2010 20 20
    21. 21. Depression Data 7 Symptom Factors Physical: Performance: Psychological: E Sleep M, L Sleep Energy Work & Interests 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) Predictive Medicine, Inc. © 2010 21 21
    22. 22. Overview Recovery Model and Parameters W A C M Predictive Medicine, Inc. © 2010 E ES MS 22 22
    23. 23. Recovery Equation (Luciano Model) = + + + Predictive Medicine, Inc. © 2010 23 23
    24. 24. Example Patient (CBT) Individual Patient Recovery Pattern and Error Fit of Model on for individual patient captures trends but 24 not entire pattern. Not good enough. Predictive Medicine, Inc. © 2010 24
    25. 25. Patient Group (CBT) Recovery Pattern and Error Model on data for patient treatment group captures 25 entire pattern. Good Predictive Medicine, Inc. © 2010fit of Model to data. 25
    26. 26. Latency Predictive Medicine, Inc. © 2010 26 26
    27. 27. Treatment Effects and Interaction Effects CBT Sequential DMI: •Interactions > 2x •Loops Predictive Medicine, Inc. © 2010 DMI (delayed) CONCURRENT 27 27
    28. 28. 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. © CBT (talk: no drugs) 2010 DMI (drug: tricyclic antidepressant) 28
    29. 29. 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 29 29
    30. 30. Give me a break!!! One more slide (so you see what’s coming when we return) Predictive Medicine, Inc. © 2010 30 30
    31. 31. Inside the Overview 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? 1. Tools to Integrate Biomedical Data By Hand Using Tools Automated 1. Knowledge Standards and Best Practices that enable web scale Integration Connecting data 5 Stars 5 Stars not enough Predictive Medicine, Inc. © 2010 31 31
    32. 32. Give me a break!!! Predictive Medicine, Inc. © 2010 32 32
    33. 33. Inside the Overview 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? 1. Tools to Integrate Biomedical Data By Hand Using Tools Automated 1. Knowledge Standards and Best Practices that enable web scale Integration Connecting data 5 Stars 5 Stars not enough Predictive Medicine, Inc. © 2010 33 33
    34. 34. 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 34
    35. 35. Data, Not Programs 12 35 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html. 35
    36. 36. Data, Not Programs 12 Feet? Feet? Years? Years? December? December? Noon? Noon? Dozen? Dozen? 36 36 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html. 36 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html.
    37. 37. Data, Not Programs 1749 1749 1749 1749 1749 1749 1749 1749 1749 1749 1749 1749 1750 1750 1750 1750 1750 1750 1750 1750 1750 1750 1750 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 M O N TH LY M EAN SU N SPO T N U M BER S ======================================================== ======================= Y ear Jan Feb M ar A pr M ay Jun Jul A ug S ep O ct N ov D ec ------------------------------------------------------------------------------1 7 4 9 5 8 .0 6 2 .6 7 0 .0 5 5 .7 8 5 .0 8 3 .5 9 4 .8 6 6 .3 7 5 .9 7 5 .5 1 5 8 .6 8 5 .2 1 7 5 0 7 3 .3 7 5 .9 8 9 .2 8 8 .3 9 0 .0 1 0 0 .0 8 5 .4 1 0 3 .0 9 1 .2 6 5 .7 6 3 .3 7 5 .4 5 8 .0 6 2 .6 7 0 .0 5 5 .7 8 5 .0 8 3 .5 9 4 .8 6 6 .3 7 5 .9 1 7 5 1 7 0 .0 4 3 .5 4 5 .3 5 6 .4 6 0 .7 5 0 .7 6 6 .3 5 9 .8 2 3 .5 2 3 .2 2 8 .5 4 4 .0 7 5 .5 1 7 5 2 3 5 .0 5 0 .0 7 1 .0 5 9 .3 5 9 .7 3 9 .6 7 8 .4 2 9 .3 2 7 .1 4 6 .6 3 7 .6 4 0 .0 1 5 8 .6 1 7 5 3 4 4 .0 3 2 .0 4 5 .7 3 8 .0 3 6 .0 3 1 .7 2 2 .0 3 9 .0 2 8 .0 2 5 .0 2 0 .0 6 .7 8 5 .2 1754 0 .0 3 .0 1 .7 1 3 .7 2 0 .7 2 6 .7 1 8 .8 1 2 .3 8 .2 2 4 .1 1 3 .2 4 .2 7 3 .3 1 7 5 5 1 0 .2 1 1 .2 6 .8 6 .5 0 .0 0 .0 8 .6 3 .2 1 7 .8 2 3 .7 6 .8 2 0 .0 7 5 .9 8 9 .2 1 7 5 6 1 2 .5 7 .1 5 .4 9 .4 1 2 .5 1 2 .9 3 .6 6 .4 1 1 .8 1 4 .3 1 7 .0 9 .4 8 8 .3 1 7 5 7 1 4 .1 2 1 .2 2 6 .2 3 0 .0 3 8 .1 1 2 .8 2 5 .0 5 1 .3 3 9 .7 3 2 .5 6 4 .7 3 3 .5 9 0 .0 1 7 5 8 3 7 .6 5 2 .0 4 9 .0 7 2 .3 4 6 .4 4 5 .0 4 4 .0 3 8 .7 6 2 .5 3 7 .7 4 3 .0 4 3 .0 1 0 0 .0 1 7 5 9 4 8 .3 4 4 .0 4 6 .8 4 7 .0 4 9 .0 5 0 .0 5 1 .0 7 1 .3 7 7 .2 5 9 .7 4 6 .3 5 7 .0 8 5 .4 1 7 6 0 6 7 .3 5 9 .5 7 4 .7 5 8 .3 7 2 .0 4 8 .3 6 6 .0 7 5 .6 6 1 .3 5 0 .6 5 9 .7 6 1 .0 1 0 3 .0 9 1 .2 1 7 6 1 7 0 .0 9 1 .0 8 0 .7 7 1 .7 1 0 7 .2 9 9 .3 9 4 .1 9 1 .1 1 0 0 .7 8 8 .7 8 9 .7 6 5 .7 37 4 6 .0 6 3 1. Webopedia. “Data 2Dictionary.”2Available online at9 www.webopedia.com/TERM/d/data_dictionary.html. .3 176 4 3 .8 7 .8 4 5 .7 6 0 .2 3 .9 7 7 .1 3 3 .8 6 7 .7 6 8 .5 6 9 .3 7 7 .8 7 7 .2 37
    38. 38. Data, Not Programs Data Dictionaries: Without a data dictionary, a database management system [or any program] cannot access data from the database.”1 Duh! 38 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html. 38
    39. 39. Data, Not Programs Data Dictionaries: Without a data dictionary, a database management system [or any program] cannot access data from the database.”1 Duh! 39 1. Webopedia. “Data Dictionary.” Available online at www.webopedia.com/TERM/d/data_dictionary.html. 39
    40. 40. Metadata (simplified) Biochemical Reaction Synonyms <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> <species id=“pyruvate” metaid=“pyruvate”> <annotation xmlns:bp=“http://biopax.org/releas <bp:smallMolecule rdf:ID=“#pyruvate” > <bp:SYNONYMS>pyroracemic acid</bp:SYNO <bp:SYNONYMS>2-oxo-propionic acid</bp:S <bp:SYNONYMS>alpha-ketopropionic acid</b <bp:SYNONYMS>2-oxopropanoate</bp:SYNO <bp:SYNONYMS>2-oxopropanoic acid</bp:S <bp:SYNONYMS>BTS</bp:SYNONYMS> <bp:SYNONYMS>pyruvic acid</bp:SYNONYM </bp:smallMolecule> </annotation> </species> </reaction> 40 40
    41. 41. 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 41 41
    42. 42. Metadata (Webified) Query results return links to the original data! Adapted from Mark Wilkinson webscience20-120829124752-phpapp01 42
    43. 43. Data Sharing (Shafu) Predictive Medicine, Inc. © 2010 43
    44. 44. Had enough for now? Ready to start getting your hands dirty? Predictive Medicine, Inc. © 2010 44 44
    45. 45. CV Background slides... 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 45
    46. 46. Whew! Now that was fun, wasn’t it? Any questions? Predictive Medicine, Inc. © 2010 46 46
    47. 47. Workshop 1995 Book 1996 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 Predictive Medicine, Inc. © 2010 47
    48. 48. 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 • Proté gé • Gruff & Allegrograph Predictive Medicine, Inc. © 2010 48 48
    49. 49. 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 49
    50. 50. 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 50

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