Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB 2006)

Tuusula, Finland, 17-18 June 2006

Topics Accepted papers Important dates Invited speakers Organizers Location Registration Program



The ever-ongoing growth in the amount of biological data, the development of genome-wide measurement technologies, and the shift from the study of individual genes to systems view all contribute to the need to develop computational techniques for learning models from data. At the same time, the increase in available computational resources has enabled new, more realistic modeling methods to be adopted.

In bioinformatics, most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. In many cases these structures are naturally described by probabilistic graphical models, such as Hidden Markov Models, Conditional Random Fields or Bayesian Networks. Recently, approaches that combine Support Vector Machines and probabilistic models have been introduced (Fisher kernels, Max-margin Markov Networks, Structured SVM). These techniques benefit from efficient convex optimization approaches and thus are potentially well-scalable to large problems in bioinformatics.

The increasing amount of high-throughput experimental data begins to enable the use of these advanced modelling methods in bioinformatics and systems biology. At the same time new computational challenges emerge. Statistical methods are required to process the data so that underlying potentially complex statistical patterns can be discerned from spurious patterns created by random effects. At its simplest this problem calls for data normalization and statistical hypothesis testing, in the more general case, one is required to select a model (e.g. gene network) that best explains the data.


The aim of this workshop is to provide a broad look at the state of the art in the probabilistic modeling and machine learning methods involving biological structures and systems, and to bring together method developers and experimentalists working with the problems.

We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule/cellular structures) and methods supporting genome-wide data analysis.

A non-exhaustive list of topics suitable of this workshop:



Organization and related workshops

The workshop is organized by the European Network of Excellence PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) and belongs to the thematic programme on 'Learning with Complex and Structured Outputs'.

The workshop is preceded by the Fourth International Workshop on Computational Systems Biology(WCSB 2006, June 12-13, Tampere, Finland).

The workshop is immediately followed by the International Specialised Symposium on Yeasts (ISSY25, June 18-21, Espoo, Finland) that has the theme 'Systems biology of Yeast - From Models to Applications'.


The abstract submission is over. The list of accepted papers/posters can be found here. Final versions are due May 31. Workshop proceedings containing the extended abstracts of talks and posters will be published by University of Helsinki.

Selected papers from the workshop will be published in BMC Bioinformatics special issue. BMC Bioinformatics has impact factor 5.42, which makes it the second highest ranked bioinformatics journal. There will be a separate call for submissions to the special issue.

Important dates

Invited Speakers (confirmed)

Organizing Committee

Local Organization



Previous update: 20.06.2006 20:41 - Veli Mäkinen