PSB Session on Personalized Medicine Call For Papers


Call for Papers and Posters
Personalized Medicine:
from genotypes and molecular phenotypes towards computed therapy

January 3-7, 2012
Fairmont Orchid Resort, Kohala Coast
The Big Island of Hawaii, U.S.A.


 

Motivation      


Genotyping and large-scale molecular phenotyping are already available for large patient cohorts and will soon become routinely available for all patients. These data are setting the stage for rapid advances in personalized medicine, enabling better disease classification, more precise treatment, and improved screening for disease prevention.

Robust statistical and computational methods for analyzing these data are critical to realizing the promises of personalized medicine. Important analysis problems include: identifying and correcting for hidden structure; dealing with missing data, data heterogeneity; and addressing the problem of multiple testing. For example, in Genome-Wide Association studies (GWAS), population structure and family relatedness can reduce power and cause spurious associations. In gene expression studies, experimental artifacts or environmental influences, have been shown to corrupt results of na´ve analyses. Additionally, hypothesizing and inferring hidden phenotypes, such as cell type or transcription factor activity help reveal hidden structure. Further advances in statistical modeling and machine learning are still needed to realize the promise of personalized medicine of delivering a "computed therapy".

This session focuses on methods to address open and new methodological problems pertaining to genotype data, intermediate phenotypes, clinical variables and disease. Problem areas within the scope include methods for Genome-Wide Association Studies, gene expression modeling with relevance to disease and causal modeling integrating information from genotype, intermediate phenotype and disease labels. The session is intended to have a broad target audience including method developers and practitioners in the fields of medical and human genetics, statistical genetics and related areas.


Session Topic

We would like to invite contributions with relevance to improving statistical and computational methodology in personalized medicine that describe either (1) a new problem including ideas on how to tackle them, (2) a methodological improvement over solutions to existing problems alongside empirical evaluation, (3) adaptations of existing solutions to datasets with real-world scale. We encourage submissions that span the full range from genotype to intermediate phenotype to disease phenotype. The focus will be on methods applicable to large, real-world problems. Both frequentist and Bayesian perspectives will be welcome.
Examples of topics and problems within the scope of this session include :


Other topics within the subject area are welcome.

Session Co-Chair


Jennifer Listgarten, Ph.D.
Microsoft Research (Los Angeles)
jennl@microsoft.com
Oliver Stegle, Ph.D.
Max Planck Institutes, Tuebingen
oliver.stegle@tuebingen.mpg.de
Fritz Roth, Ph.D.
University of Toronto
fritz.roth@gmail.com
Quaid Morris, Ph.D.
University of Toronto
quaid.morris@utoronto.ca

Submission Information

 
Please note that the submitted papers are reviewed and accepted on a competitive basis.
 

Important Dates 

Paper Format

Please see the PSB paper format template and instructions at http://psb.stanford.edu/psb-online/psb-submit.

The file formats we accept are: postscript (*.ps) and Adobe Acrobat (*.pdf)). Attached files should be named with the last name of the first author (e.g. altman.ps or altman.pdf). Hardcopy submissions or unprocessed TeX or LaTeX files will be rejected without review.

Each paper must be accompanied by a cover letter. The cover letter must state the following:
 
Submitted papers are limited to twelve (12) pages in our publication format. Please format your paper according to instructions found at http://psb.stanford.edu/psb-online/psb-submit/. If figures cannot be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit.