Discussion

Suggestions for Discussion

• General validation techniques have to be integrated within the framework:
  • To be able to perform the required task, learning & prediction has to be kept completely separate from validation techniques
    • I.e. the validation approaches are managing the prediction methods
  • For this an internal reporting/prediction format has to be found or agreed on
    • What kind of format (-> WP1)
  • What existing validation routines from project members are out there?
    • IST lazar, IDEA AMBIT, NTUA Y-scrambling
    • And how to integrate them
• A substantial amount of statistical tests will have to be performed
  • Can we agree for R, as underlying statistical toolkit
  • Are there other competitive, OpenSource, projects or programs available?
• Greatest challenge: Validation against confidential data
  • How can this be achieved?
    • Convince advisory board members
    • Public and artificial data has to be transferred on a non-networked supply chain
      • Test are run locally (in house), some type of reports will have to sent back. however:
    • However: Reporting should not allow disclosure of underlying confidential DB
      • Is this an issue ?
      • How to compare different in-house DBs?
  • Is it possible or even wanted, to have a confidential - confidential validation?
    • Companies will not share their data
• Guidelines
  • OECD guidelines (http://ecb.jrc.ec.europa.eu/DOCUMENTS/QSAR/OECD_ENV_JM_MONO(2007)2.pdf)
    • Which one to incorporate and how?
  • Or use ECB QSAR model reporting format (QPRF: http://ecb.jrc.it/qsar/qsar-tools/qrf/QPRF_version_1.1.pdf)
    • Is it sufficient?