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NIPS 2011 workshop on

"From Statistical Genetics to Predictive Models in Personalized Medicine (NIPS PM 2011)"

at Hotel Melia Sol y Nieve: Slalom Room

Sierra Nevada, Spain, December 16, 2011

URL: http://agbs.kyb.tuebingen.mpg.de/wikis/bg/NIPSPM11

supported by the PASCAL2 Network

Important Dates:

Confirmed Invited Speakers:


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.

Workshop schedule (December 16, 2011)

7.30-7.35 Welcome and Introduction
Workshop Chairs
7.35-8.25 Keynote: From Genomes to Personalized Medicine: Reverse Engineering Biological Systems
Pierre Baldi
8.25-9.15 Keynote: New advances in the genetics of melanoma: how can it contribute to personalized medicine?
Florence Demenais
9.15-10.05 Poster spotlight, poster session and coffee break
Jose Leiva-Murillo, Jesse Davis, Anima Singh, Yonatan Halpern
10.05-10.30 Detecting similar high-dimensional responses to experimental factors from human and model organism
Tommi Suvitaival
16.00-16.05 Welcome and Introduction
Workshop Chairs
16.05-16.55 Keynote: From bed to bench and back: current perspectives of personalized medicine in psychiatry
Bertram Müller-Myhsok
16.55-17.20 Inferring a measure of physiological age from multiple ageing related phenotypes
David Knowles
17.20-18.20 Poster spotlight, poster session and coffee break
Ali Faisal, Jenna Wiens, Christoph Lippert, Gad Abraham, Norman Poh
18.20-18.45 Accuracy test for genome wide selection of bio-markers
Adam Kowalczyk
18.45-20.00 Panel discussion
Invited Speakers

Accepted contributions:

Oral presentations

Inferring a measure of physiological age from multiple ageing related phenotypes
David Knowles (Knowles_etal.pdf)
Accuracy test for genome wide selection of bio-markers
Adam Kowalczyk (Kowalcyzk_etal.pdf)
Detecting similar high-dimensional responses to experimental factors from human and model organism
Tommi Suvitaival (Suvitaival_etal.pdf)

Poster spotlights

Sparse Linear Models Explain Phenotypic Variation and Predict Risk of Complex Disease
Gad Abraham (Abraham_etal.pdf)
Discovering Latent Structure in Clinical Databases
Jesse Davis (Davis_etal.pdf)
Biomarker discovery via dependency analysis of multi-view functional genomics data
Ali Faisal (Faisal_etal.pdf)
Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models
Jose Leiva-Murillo (LeivaM?_etal.pdf)
FaST? linear mixed models for genome-wide association studies
Christoph Lippert (Lippert_etal.pdf)
Modeling Rate of Change in Renal Function for Individual Patients: A Longitudinal Model Based on Routinely Collected Data
Norman Poh (Poh_etal.pdf)
Learning Classification Trees for Personalized Cardiovascular Risk Stratification
Anima Singh (Singh_etal.pdf)
On the Promise of Topic Models for Abstracting Complex Medical Data: A Study of Patients and their Medications
Jenna Wiens (Wiens_etal.pdf)
Patient Surveillance Algorithms for the Emergency Department
Yonatan Halpern (Halpern_etal.pdf)

Topics of Interest:

We welcome submissions on any aspect of machine learning in statistical genetics and personalized medicine. We would like to encourage submissions on any of (but not limited to) the following topics:

Submission Instructions:

The deadline for submissions has now passed.