The scientific program will be composed of several sessions of invited and contributed talks.

The complete workshop proceedings can be downloaded from here .

Eleazar Eskin, University of California, Los Angeles, USA.

Known and Unknown Confounding in Genetic Studies

Variation in human DNA sequences account for a significant amount of genetic risk factors for common disease such as hypertension, diabetes, Alzheimer's disease, and cancer. Identifying the human sequence variation that makes up the genetic basis of common disease will have a tremendous impact on medicine in many ways. Recent efforts to identify these genetic factors through large scale association studies which compare information on variation between a set of healthy and diseased individuals have been remarkably successful. However, despite the success of these initial studies, many challenges and open questions remain on how to design and analyze the results of association studies. As several recent studies have demonstrated, confounding factors such as batch effects, population structure, and measurement errors can complicate genetics analysis by causing many spurious associations. Yet little is understood about how these confounding factors affect analyses and how to correct for these factors. In this talk I will discuss several recently developed methods based on linear mixed models for correcting for both known and unknown confounding factors in genetic studies.

Eleazar Eskin�s research focuses on developing computational methods for analysis of genetic variation. He is currently an Associate Professor in the Computer Science and Human Genetics departments at the University of California Los Angeles. Previously, he was an Assistant Professor in Residence in Computer Science Engineering at the University of California, San Diego. Eleazar completed his Ph. D. in the Computer Science Department of Columbia University in New York City. After graduation, he spent one year in the Computer Science Department at the Hebrew University in Jerusalem, Israel.


Magnus Rattray, The University of Sheffield, Sheffield, UK.

Modeling Gene Expression Time-Series with Bayesian Non-Parametrics

Bayesian non-parametric methods are a natural approach to fitting models with continuous parameters or unbounded parameter set cardinality. We are applying these methods to diverse models of time-series gene expression data. Example applications include differential equation models of transcriptional regulation, clustering data sampled at uneven times and phylogenetic models of gene expression change over evolutionary time. We use continuous-time Gaussian processes to model the time-evolution of gene expression and protein activation/concentration in time. Dirichlet processes can also be used to model an unbounded set of Gaussian process models. I will present results where we apply these methods to gene expression time-course data from embryonic development in Drosophila.

This is joint work with Neil Lawrence, Antti Honkela, Michalis Titsias and James Hensman.

BSc in Mathematics and Physics (1992) and PhD in Computer Science (1996) from the University of Manchester. Postdoctoral studies on statistical mechanics theory of on-line learning in the Neural Computing Research Group, Aston University (1996-1998). Lecturer and Senior Lecturer in the School of Computer Science at the University of Manchester (1998-2010) working on statistical inference with applications to problems in computational molecular biology and phylogenetic inference. Appointed Collaborative Chair in Neuro- and Computer Science at the University of Sheffield in 2010. I co-lead research groups in Machine Learning and Computational Biology and my current work is on probabilistic modelling and statistical inference with applications in functional genomics and systems biology.


Robert Küffner, Ludwig-Maximilians-Universität, Munich, Germany.

Successful Strategies to Gene Regulatory Network Inference

The inference of gene regulatory networks from mRNA expression data is characterized by the development of many different approaches with their specific performances, data requirements, and inherent biases.�Based on a recent community-wide challenge, the Dialogue on Reverse Engineering Assessment and Methods (DREAM), the so far largest assessment of inference approaches has been conducted. The accuracy of predictions was evaluated against experimentally supported interactions in the procaryote model organism E. coli, the eucaryote model organism S. cerevisiae and in silico target systems. Over thirty independently contributed methods were analyzed including well-known approaches based on lasso, mutual information and Bayesian networks but also a range of novel strategies. For instance, the novel algorithms based on random forests and ANOVA outperformed established tools significantly. Further analysis revealed not only which inference strategies are particularly successful but also which kind of specific information was utilized from the different types of experimental measurements. At the same time, the performance of the individual approaches varied in different network motifs or target systems. By integrating individual predictions into community predictions the performance improved overall and became markedly more robust. This principle is known as the wisdom of crowds: a solution derived from a community of independent decision makers will be better, on average, than any individual solution. Based on community predictions, we also constructed the first comprehensive gene regulatory model for the human pathogen Staphylococcus aureus.

Dr. Robert Küffner habilitated in informatics in 2010 and is currently a group leader for computer science and bioinformatics at the Ludwig-Maximilians Universit�t M�nchen. He received his PhD in molecular biology in 1998 at the Heinrich-Heine Universit�t in D�sseldorf, Germany. His main interests include the analysis of biological networks via Petri Nets as well as the areas of text mining, expression analysis, gene regulation, alternative splicing and systems biology.
Recently, Dr. Küffner's team was recognized as best performer in two international community-wide challenges (DREAM4/2009 and DREAM5/2010) where comprehensive blinded assessments of network inference approaches have been conducted.

Poster presentations