http://www.dagstuhl.de/17472

November 19 – 22 , 2017, Dagstuhl Seminar 17472

Addressing the Computational Challenges of Personalized Medicine

Organizers

Niko Beerenwinkel (ETH Zürich – Basel, CH)
Holger Fröhlich (UCB Biosciences GmbH – Monheim, DE)
Franziska Michor (Harvard Medical School – Boston, US)
Susan A. Murphy (Harvard University – Cambridge, US)

For support, please contact

Susanne Bach-Bernhard for administrative matters

Michael Gerke for scientific matters

Dagstuhl Reports

As part of the mandatory documentation, participants are asked to submit their talk abstracts, working group results, etc. for publication in our series Dagstuhl Reports via the Dagstuhl Reports Submission System.

Documents

List of Participants
Shared Documents
Dagstuhl Seminar Wiki
Dagstuhl Seminar Schedule [pdf]

(Use seminar number and access code to log in)

Motivation

Personalized, precision, P4 or stratified medicine is understood as a non-traditional medical approach, in which patients are stratified and dynamically re-stratified based on their disease subtype, disease risk, disease prognosis or treatment response using specialized diagnostic tests. The topic is of highest relevance to the pharma and biotech industry and for the health care sector as a whole. Opportunities include better medication effectiveness, reduction of adverse drug events, lower health costs, earlier disease detection and prevention, better disease management and smarter design of clinical trials.

Personalized medicine is deeply connected with and depended on computational algorithms and models that can deal with the quickly growing volume of large scale and high dimensional data in the health care sector (“big data”). Examples of used data include various -omics data types (genomics, transcriptomics, proteomics, metabolomics), bio-images (e.g. MRT and CT scans), electronic medical records (EMRs), health claims data from insurance companies, and data from wearable devices and mobile health applications. Computational approaches are developed within different science fields, such as computational statistics, machine learning, data mining, and mathematical modeling and simulation. These approaches are frequently used to identify patient sub-groups (e.g. via cluster analysis) and to predict clinical or therapeutic health outcomes (e.g. via supervised machine learning methods or via simulation of mechanistic mathematical models).

Despite the technological advancements in machine learning and data mining over the last decade personalized medicine is only a partial reality in clinical practice, and computational methods are a key to most of the reasons behind. Therefore, further developments in this area are required. The central goal of this Dagstuhl Seminar is to bring together leading computational scientists from different fields (computer science, bioinformatics, computational statistics, computational systems biology) to discuss how the existing computational challenges in personalized medicine could be better addressed in the future. In addition, contributions by few selected non-computational scientists (medical scientists, pharmacologists, behavioral scientists) will close the gap to the application field.

The 3-day seminar program will specifically focus on the following topics:

  • Day 1: Enhancing prediction performance of computational models
  • Day 2: Improving interpretability of computational models
  • Day 3: Validation of models and implementation into clinical routine work

The seminar will be organized into two main sessions per day chaired by one of the organizers. At the beginning of each day there will be one keynote talk. In addition, each of the participants will be asked to give a 5 minutes talks to introduce his work at the beginning of the first day. There will be the possibility for small breakout sessions, which can run in parallel to the main session.

Results of the seminar will be made visible via the Dagstuhl Reports as well as via a Perspectives paper in a leading open access journal, such as e.g. PLoS Computational Biology. Further desirable outcomes include press releases as well as joint follow-up publications, grant applications and meetings by at least a subset of invitees.

License
  Creative Commons BY 3.0 DE
  Niko Beerenwinkel, Holger Fröhlich, Franziska Michor, and Susan A. Murphy

Classification

  • Artificial Intelligence / Robotics
  • Bioinformatics
  • Modelling / Simulation

Keywords

  • Data science
  • Computational modelling
  • Bioinformatics
  • Systems biology
  • Personalized medicine

Book exhibition

Books from the participants of the current Seminar 

Book exhibition in the library, ground floor, during the seminar week.

Documentation

In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.

 

Download overview leaflet (PDF).

Publications

Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.

NSF young researcher support