Call for Contributions:
NIPS 2011 workshop on
“From Statistical Genetics to Predictive Models in Personalized Medicine (NIPS PM 2011)”
Granada, Spain, December 16 or 17, 2011
* Deadline for submissions: October 17, 2011
* Notification of acceptance: October 31, 2011
Confirmed Invited Speakers:
* Prof. Dr. Joaquin Dopazo, Head of the Bioinformatics and Genomics Department, CIPF, Valencia, Spain
* Prof. Dr. Bertram Müller-Myhsok, Head of Statistical Genetics Lab, Max Planck Institute for Psychiatry, Munich, Germany
Technological advances to profile medical patients have led to a change of paradigm in medical prognoses. Medical diagnostics carried out by medical experts is increasingly complemented by large-scale data collection and quantitative genome-scale molecular measurements. Data that are already available as of today or are to enter medical practice in the near future include personal medical records, genotype information, diagnostic tests, proteomics and other emerging ‘omics’ data types.
This rich source of information forms the basis of future medicine and personalized medicine in particular. Predictive methods for personalized medicine allow to integrate these data specific for each patient (genetics, exams, demographics, imaging, lab, genomic etc.), both for improved prognosis and to design an individual-specific optimal therapy.
However, the statistical and computational approaches behind these analyses are faced with a number of major challenges. For example, it is necessary to identify and correcting for structured influences within the data; dealing with missing data and the statistical challenges that come along with carrying out millions of statistical tests. Also, to render these methods useful in practice computational efficiency and scalability to large-scale datasets are an integral requirement. Finally, any computational approach needs to be tightly integrated with medical practice to be actually used and the experiences gained need to be fed back into future development and improvements.
To both address these technical difficulties ahead and to allow for an efficient integration and application in a medical context, it is necessary to bring the communities of statistical method developers, medics and biological investigators together.
The purpose of this cross-discipline workshop is to bring together machine learning and healthcare researchers interested in problems and applications of predictive models in the field of personalized medicine. The goal of the workshop will be to bridge the gap between the theory of predictive models and the applications and needs of the healthcare community. There will be exchange of ideas, identification of important and challenging applications and discovery of possible synergies. Ideally this will spur discussion and collaboration between the two disciplines and result in collaborative grant submissions. The emphasis will be on the mathematical and engineering aspects of predictive models and how it relates to practical medical problems.
Although, predictive modeling for healthcare has been explored by biostatisticians for several decades, this workshop focuses on substantially different needs and problems that are better addressed by modern machine learning technologies. For example, how should we organize clinical trials to validate the clinical utility of predictive models for personalized therapy selection? This workshop does not focus on issues of basic science; rather, we focus on predictive models that combine all available patient data (including imaging, pathology, lab, genomics etc.) to impact point of care decision making.
Topics of Interest:
We would like to encourage submissions on any of (but not limited to) the following topics:
* Preventive medicine
* Therapy selection
* Statistical genetics
* Medical genetics
* Precision diagnostics (more precise diagnostics, diseases sub-typing)
* Companion diagnostics/Therapeutics
* Patient risk assessment (for incidence of diseases)
* Personalized medicine
* Integrated diagnostics combining multiple modalities like imaging, genomics and in-vitro diagnostics
We call for paper contributions of up to 8 pages to the workshop using NIPS style. Accepted papers will be presented at the poster session with an additional poster spotlight presentation or full oral presentation. One author of every accepted paper has to be present to present poster and spotlight/talk.
The link to the submission system will be available at http://agbs.kyb.tuebingen.mpg.de/wikis/bg/NIPSPM11
* Karsten Borgwardt, Max Planck Institutes, Germany
* Oliver Stegle, Max Planck Institutes, Germany
* Shipeng Yu, Siemens Healthcare, USA
* Glenn Fung, Siemens Healthcare, USA
* Faisal Farooq, Siemens Healthcare, USA
* Balaji Krishnapuram, Siemens Healthcare, USA