• March 18-22, San Francisco

    2013 Joint Summits on Translational Science

2013 Joint Summits Tutorials

TBI-CRI Tutorials CME/CE eligibility

Tutorials are CME/CE eligible and incur an additional fee beyond the registration fee. Tutorial fees are not included in the registration fee.

 

Tutorial CME/CE eligible credits Full & MemberAdvantage Non-member
Half-Day sessions 3 $95 $150

Monday, March 18, 8:30 a.m. – 12:00 p.m.

T-1: Introduction to Translational Bioinformatics

Faculty: Maricel G. Kann, University of Maryland, Baltimore County; Yves A. Lussier, The University of Illinois in Chicago
Content level: 20% Basic, 50% Intermediate, 30% Advanced

Fee: $95 member $150 non-member CME=3

In 2005, Dr. Elias Zerhouni, Director of the National Institutes of Health (NIH), wrote "It is the responsibility of those of us involved in today’s biomedical research enterprise to translate the remarkable scientific innovations we are witnessing into health gains for the nation... At no other time has the need for a robust, bidirectional information flow between basic and translational scientists been so necessary." Clearly evident in Dr. Zerhouni’s quote is the role biomedical informatics needs to play in facilitating translational medicine. American Medical Informatics Association (AMIA) now hosts the Joint Summits on Translational Science of which the Summit on Translational Bioinformatics is one of the two components. This tutorial is designed to teach the basics of the various types of molecular data and methodologies currently used in bioinformatics and genomics research, and how these can interface with clinical data. This tutorial will address the hypotheses one can start with by integrating molecular biological data with clinical data, and will show how to implement systems to address these hypotheses. The tutorial will cover real-world case-studies of how genetic, genomics, and proteomic data has been integrated with clinical data.

By the end of the tutorial, participants will be able to:

  • Understand why biologists and clinicians use each measurement technology, and the advantages of each.
  • appropriate for studying diseases.
  • Be able to list high-level requirements for an infrastructure relating research and clinical genetic and genomic data.

Outline of Topics:

  • Basic understanding of various genome-scale measurement modalities: sequencing, polymorphisms, haplotypes, proteomics, gene expression, metabolomics, and others
  • Crucial difference between genetic and genomic data
  • Nature and format of expression, polymorphism, proteomics, and sequencing data
  • Overview of the most commonly used structured vocabularies, taxonomies, and ontologies used in genomics research
  • Description of the most frequently used analysis and clustering techniques
  • How the genetic predisposition to disease is studied
  • Use of genetic information across medical specialties
  • How to find clinical genetic tests
  • Genomic and clinical data to study patient disease-free status and survival
  • How informatics can be used to identify potential drug targets
  • Types of biomarkers
  • Parallels between research methods in medical informatics and bioinformatics
  • Relating clinical measurements with molecular measurements

 

T-2: Using Ontologies for Data-driven Medicine

Faculty: Nigam H. Shah, MBBS, PhD, Stanford Center for Biomedical Informatics Research; R. Harpaz, Stanford University
Content level: 20% basic; 40% intermediate; 40% advanced.

Fee: $95 member $150 non-member CME=3

The tutorial will draw on content from a graduate course on Data-driven Medicine at Stanford Intended Audience http://bmi215.stanford.edu: Scientists and researchers seeking to understand how to use ontologies for data-mining. Health IT System developers and investigators seeking to understand how to leverage ontologies.

Data-driven medicine envisions the discovery of new treatment options based on the multi-model molecular measurements on patients and learning from the trends hidden among the diagnoses, prescriptions, and discharge summaries of millions of patient encounters logged by clinical practitioners. Biomedical ontologies are widely used across the whole spectrum of this research enterprise. The National Center for Biomedical Ontology (NCBO) enables users to access biomedical terminologies, create lexicons, index the contents of online data sets with controlled terms (data annotation), and to recommend particular ontologies appropriate for specific data-annotation tasks. This tutorial will demonstrate the use of NCBO technology in data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies. We will review how to use the resulting high-throughput data for earning practice-based evidence, for conducing drug safety studies, and for building predictive models.

We will discuss in depth how to investigate adverse drug event associations by analyzing textual notes in a clinical data warehouse. We will discuss how such an analysis of textual clinical notes could detect adverse drug events before the official alert, can detect known drug-drug interactions with high accuracy, and can rapidly examine drug-event associations (such as between proton-pump inhibitor drugs and myocardial infarction) that are hypothesized based on experimental results. In addition, we will review a proof of principle study which shows the potential of text-analytics to uncover ‘natural experiments’ that profile the safety of Cilostazol in patients with peripheral artery disease, and agree with previously published controlled studies.

This tutorial will provide participants with in-depth understanding of how ontologies and terminologies are used to execute diverse research use cases in clinical and translational research.

By the end of the tutorial, participants will be able to:

  • Understand NCBO technology for clinical and translational research
  • Understand the utility of ontologies for data-mining
  • See first-hand examples of the unreasonable effectiveness of data
  • Learn how to use ontologies in learning practice-based evidence

Outline of Topics:

  • Key NCBO technology used
  • Generation of a de-identified patient feature matrix
  • Overview of pharmacovigilence using clinical notes.
  • Case study: Proton pump inhibitors and risk of myocardial infarction
  • Network analysis of EHR data to uncover "natural experiments"
  • Practice-based evidence: profiling the safety of Cilostazol

 

T- 3: Research Informatics: From Lab to Laptop

Faculty: Philip R.O. Payne, PhD; Peter J. Embi, MD., MS; The Ohio State University, Department of Biomedical Informatics, Columbus, OH

Fee: $95 member $150 non-member CME=3

A common substrate underlying the conduct of trans-disciplinary research is the use of biomedical informatics theories and methods, as well as a variety of computational technologies. Common information needs that are targeted in such contexts include supporting team collaboration, project planning, data management, knowledge generation, and results dissemination. In this tutorial, we will provide researchers, decision makers, and technical staff with an overview of the core definitions, theories, and best practices that collectively contribute to the successful application of biomedical informatics in order to facilitate and enable biomedical research.

We will use a set of vignettes to illustrate common challenges and opportunities in this domain, including: 1) the design of data structures and the encoding of information and knowledge so as to support T1-translation; 2) the integrative analysis and visualization of bio-molecular and clinical phenotype data; and 3) the instrumentation of enterprise systems to enable and facilitate a learning healthcare system in which every patient encounter is an opportunity to generate new clinical evidence via the unification of disparate and complementary data sources and analytical techniques.

 

T-4 Part A - Navigating the funding landscape: How to find, develop and write your first proposal

Faculty: Russ B. Altman, MD, PhD, Stanford Center for Biomedical Informatics Research; Sean Mooney, PhD, Buck Institute for Research on Aging
Content level: 50% basic; 40% intermediate; 10% advanced

Fee: $95 member $150 non-member CME=3

 

Your departmental chair has asked you to develop your first funding proposal. In this presentation, the process of finding and writing a topical and fundable proposal will be described. In the first talk, Prof. Altman will describe the keys to writing a proposal narrative that will be seen as successful by the reviewers. The dos and don’ts of proposal writing will be outlined and tips for submitting competitive language of interest to the reviewers will be highlighted.

 

In the second presentation, Prof. Mooney will describe navigating the funding landscape. The process of finding NSF and NIH opportunities, deciding on a research topic and identifying appropriate reviewers will be noted. The basics of proposal preparation will illustrated through example, including conversations you will have with your university administrators and NIH program staff. The resources to find funding opportunities will be distributed.

Upon completion of the two presentations, and open discussion will commence with questions from the audience.

 

By the end of the tutorial, participants will be able to:

  • Identify funding opportunities from federal agencies
  • Identify appropriate reviewers
  • Develop a proposal narrative that best illustrates your research to potential reviewers
  • Develop and prepare a proposal and what will be expected of you from your university administration

Outline of Topics:

  • Finding funding opportunities using online tools
  • Identifying the program officers who can answer questions about the proposals
  • Identifying the reviewers who will evaluate your proposal
  • The timeline for review and decisions
  • Dos and don’ts of writing the proposal narrative
  • Topics to include in the text of your narrative
  • Writing your narrative competitively

  

T-4 Part B - Mock Grant Review/Study Section

Faculty: Nick Anderson, PhD, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington; Shannon McWeeney, PhD, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
Content level: 40% basic; 40% intermediate; 20% advanced

Fee: $95 member $150 non-member CME=3

What just happened to my grant? How can I benefit from the grant peer review process? In this workshop, attendees will have the opportunity to learn about the proposal review process, including the typical dynamics of study section deliberations. The NIH study section review process will be demonstrated, and the workshop will present examples of mistakes applicants commonly make.

By the end of the tutorial, participants will be able to:

  • Understand the NIH and NSF review criteria
  • Participate in the roles assigned within study section for peer review
  • Understand NIH scoring system and funding processes
  • Prepare a summary statement for return to investigators