Biomedical Research as Part of the Digital Enterprise
Upcoming SlideShare
Loading in...5
×
 

Biomedical Research as Part of the Digital Enterprise

on

  • 280 views

Presented at the Association of Biomedical Resource Facilities Annual Meeting in Albuquerque NM March 25, 2014.

Presented at the Association of Biomedical Resource Facilities Annual Meeting in Albuquerque NM March 25, 2014.

Statistics

Views

Total Views
280
Views on SlideShare
280
Embed Views
0

Actions

Likes
0
Downloads
7
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

CC Attribution License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124 <br /> http://www.reuters.com/article/2012/03/28/us-science-cancer-idUSBRE82R12P20120328 <br />

Biomedical Research as Part of the Digital Enterprise Biomedical Research as Part of the Digital Enterprise Presentation Transcript

  • Biomedical Research as Part of the Digital Enterprise Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health
  • Disclaimer: I only started March 3, 2014 …but I had been thinking about this prior to my appointment
  • Let me start with a few factoids to get the ball rolling…
  • The Story of Meredith http://fora.tv/2012/04/20/Congress_Unplugged_ Phil_Bourne
  • 1. The Era of Open Has The Potential to Deinstitutionalize & Democratize Daniel Hulshizer/Associated Press
  • 1. The Era of Open Has The Potential to Deinstitutionalize & Democratize Daniel Hulshizer/Associated Press
  • 2. I can’t reproduce research from my own laboratory? Daniel Garijo et al. 2013 Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome PLOS ONE 8(11) e80278 .
  • 47/53 “landmark” publications could not be replicated [Begley, Ellis Nature, 483, 2012] [Carole Goble]
  • Characteristics of the Original and Current Experiment  Original and Current: – Purely in silico – Uses a combination of public databases and open source software by us and others  Original: – http://funsite.sdsc.edu/drugome/TB/  Current: – Recast in the Wings workflow system Daniel Garijo et al. 2013 Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome PLOS ONE 8(11) e80278 .
  • Considered the Ability to Reproduce by Four Classes of User  REP-AUTHOR – original author of the work  REP-EXPERT – domain expert – can reproduce even with incomplete methods described  REP-NOVICE – basic domain (bioinformatics) expertise  REP-MINIMAL – researcher with no domain expertise Garijo et al 2013 PLOS ONE 8(11): e80278
  • A Conceptual Overview of the Method Should Be Mandatory Garijo et al 2013 PLOS ONE 8(11): e80278
  • Time to Reproduce the Method Garijo et al 2013 PLOS ONE 8(11): e80278
  • 2. Its not that we could not reproduce the work, but the effort involved was substantial Any graduate student could tell you this and little has changed in 40 years Perhaps it is time we did better?
  • 3. Data are accumulating!
  • 4. We don’t know enough about how existing data are used * http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm Jan. 2008 Jan. 2009 Jan. 2010Jul. 2009Jul. 2008 Jul. 2010 1RUZ: 1918 H1 Hemagglutinin Structure Summary page activity for H1N1 Influenza related structures 3B7E: Neuraminidase of A/Brevig Mission/1/1918 H1N1 strain in complex with zanamivir [Andreas Prlic]
  • We Need to Learn from Industries Whose Livelihood Addresses the Question of Use
  • 5. Some would argue we are at an inflexion point for change  Evidence: – Google car – 3D printers – Waze – Robotics
  • From the Second Machine Age From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
  • 6. Scholarship is broken  I have a paper with 16,000 citations that no one has ever read  I have papers in PLOS ONE that have more citations than ones in PNAS  I have data sets I am proud of few places to put them  I edited a journal but it did not count for much
  • 7. The reward system is in need of repair
  • Okay… enough of the problems What are some solutions?
  • I cast the solutions in a vision … something I call the digital enterprise Any institution is a candidate as a digital enterprise, but lets explore it in the context of the academic medical center
  • Components of The Academic Digital Enterprise  Consists of digital assets – E.g. datasets, papers, software, lab notes  Each asset is uniquely identified and has provenance, including access control – E.g. publishing simply involves changing the access control  Digital assets are interoperable across the enterprise
  • Life in the Academic Digital Enterprise  Jane scores extremely well in parts of her graduate on-line neurology class. Neurology professors, whose research profiles are on-line and well described, are automatically notified of Jane’s potential based on a computer analysis of her scores against the background interests of the neuroscience professors. Consequently, professor Smith interviews Jane and offers her a research rotation. During the rotation she enters details of her experiments related to understanding a widespread neurodegenerative disease in an on-line laboratory notebook kept in a shared on-line research space – an institutional resource where stakeholders provide metadata, including access rights and provenance beyond that available in a commercial offering. According to Jane’s preferences, the underlying computer system may automatically bring to Jane’s attention Jack, a graduate student in the chemistry department whose notebook reveals he is working on using bacteria for purposes of toxic waste cleanup. Why the connection? They reference the same gene a number of times in their notes, which is of interest to two very different disciplines – neurology and environmental sciences. In the analog academic health center they would never have discovered each other, but thanks to the Digital Enterprise, pooled knowledge can lead to a distinct advantage. The collaboration results in the discovery of a homologous human gene product as a putative target in treating the neurodegenerative disorder. A new chemical entity is developed and patented. Accordingly, by automatically matching details of the innovation with biotech companies worldwide that might have potential interest, a licensee is found. The licensee hires Jack to continue working on the project. Jane joins Joe’s laboratory, and he hires another student using the revenue from the license. The research continues and leads to a federal grant award. The students are employed, further research is supported and in time societal benefit arises from the technology. From What Big Data Means to Me JAMIA 2014 21:194
  • Solution: Break Down the Silos  New policies, regulations e.g. data sharing  Economic drivers  The promise of shared data
  • Solution: Sustainability The How of Data Sharing  More credit to the data scientists  Change to funding models  Public/Private partnerships  Interagency cooperation  International cooperation  Better evaluation and more informed decisions about existing and proposed resources – How are current data being used?  Role of institutional repositories – reward institutions rather than PIs
  • Solution: Discoverability  Calls for data and software registries (e.g., DDI)  Data commons (NIH drive?)  More clinical trial data in the public domain  Facilitate accessibility and hence access to clinical data
  • Solution: Training  Calls out for training grants – new and as supplements to existing training efforts  Regional training centers (cf Cold Spring Harbor)?
  • These problems and potential solutions have been around a long time The good news is that “Big Data” has bought more attention to the problem
  • What Are Big Data?  Large datasets from high throughput experiments  Large numbers of small datasets  Data which are “ill-formed”  The why (causality) is replaced by the what  A signal that a fundamental change is taking place – a tipping point?
  • The NIH is Starting to Think About the Digital Enterprise, Witness…  You will hear all about BD2K from: – Jennie Larkin – Warren Kibbe – Dawei Lin bd2k.nih.gov
  • This is great, but what will the end product look like?
  • 1. A link brings up figures from the paper 0. Full text of PLoS papers stored in a database 2. Clicking the paper figure retrieves data from the PDB which is analyzed 3. A composite view of journal and database content results One Possible End Point 1. User clicks on thumbnail 2. Metadata and a webservices call provide a renderable image that can be annotated 3. Selecting a features provides a database/literature mashup 4. That leads to new papers 4. The composite view has links to pertinent blocks of literature text and back to the PDB 1. 2. 3. 4. PLoS Comp. Biol. 2005 1(3) e34
  • To get to that end point we have to consider the complete research lifecycle
  • The Research Life Cycle will Persist IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
  • Tools and Resources Will Continue To Be Developed IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication
  • Those Elements of the Research Life Cycle will Become More Interconnected Around a Common Framework IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication
  • New/Extended Support Structures Will Emerge IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication Commercial & Public Tools Git-like Resources By Discipline Data Journals Discipline- Based Metadata Standards Community Portals Institutional Repositories New Reward Systems Commercial Repositories Training
  • We Have a Ways to Go IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication Commercial & Public Tools Git-like Resources By Discipline Data Journals Discipline- Based Metadata Standards Community Portals Institutional Repositories New Reward Systems Commercial Repositories Training
  • Thank You! Questions? philip.bourne@nih.gov
  • NIHNIH…… Turning Discovery Into HealthTurning Discovery Into Health