Call for papers – Machine Learning in Systems Biology

Call for Papers

MLSB 2010
The Fourth International Workshop on Machine Learning in Systems Biology
15-16 October 2010, Edinburgh, Scotland


Molecular biology and all the biomedical sciences are undergoing a
true revolution as a result of the emergence and growing impact of a
series of new disciplines/tools sharing the “-omics” suffix in their
name. These include in particular genomics, transcriptomics,
proteomics and metabolomics, devoted respectively to the examination
of the entire systems of genes, transcripts, proteins and metabolites
present in a given cell or tissue type.

The availability of these new, highly effective tools for biological
exploration is dramatically changing the way one performs research in
at least two respects. First, the amount of available experimental
data is not a limiting factor any more; on the contrary, there is a
plethora of it. Given the research question, the challenge has
shifted towards identifying the relevant pieces of information and
making sense out of it (a “data mining” issue). Second, rather
than focus on components in isolation, we can now try to understand
how biological systems behave as a result of the integration and
interaction between the individual components that one can now monitor
simultaneously (so called “systems biology”).

Taking advantage of this wealth of “genomic” information has become a
conditio sine qua non for whoever ambitions to remain competitive in
molecular biology and in the biomedical sciences in general. Machine
learning naturally appears as one of the main drivers of progress in
this context, where most of the targets of interest deal with complex
structured objects: sequences, 2D and 3D structures or interaction
networks. At the same time bioinformatics and systems biology have
already induced significant new developments of general interest in
machine learning, for example in the context of learning with
structured data, graph inference, semi-supervised learning, system
identification, and novel combinations of optimization and learning

The Workshop is organized as “core – event” of Pattern Analysis,
Statistical Modelling and Computational Learning – Network of Excellence
2 (PASCAL 2,


The aim of this workshop is to contribute to the cross-fertilization
between the research in machine learning methods and their
applications to systems biology (i.e., complex biological and medical
questions) by bringing together method developers and
experimentalists. We encourage submissions bringing forward methods
for discovering complex structures (e.g. interaction networks,
molecule structures) and methods supporting genome-wide data analysis.


The workshop will take place 15-16 October 2010 at the Edinburgh
International Conference Centre and the Informatics Forum of the
University of Edinburgh. It will be part of the wokshop program of
ICSB 2010, The 11th International Conference on Systems Biology
(11-14 OCT 2010,


We invite you to submit an extended abstract of up to 4 pages
describing new or recently published (2010) results, formatted
according to the Springer Lecture Notes in Computer Science
style. Each extended abstract must be submitted online via the Easychair
submission system:

The extended abstracts will be reviewed by the scientific programme
committee. They will be selected for oral or poster presentation
according to their originality and relevance to the workshop topics.
Electronic versions of the extended abstracts will be accessible to the
participants prior to the conference, distributed in hardcopy form to
participants at the conference, and will be made publicly available
on the conference web site after the conference. However, the
book of abstracts will not be published and the extended abstracts
will not constitute a formal publication.

We expect that authors of selected contributions will be invited to
submit full papers to special issues of high-ranking
Machine Learning/Systems Biology journals.


15 May: Submission site open
25 June: deadline for submission of extended abstracts
25 July: notification of acceptance
15-16 October: workshop


A non-exhaustive list of topics suitable for this workshop is given


Machine learning algorithms
Bayesian methods
Data integration/fusion
Feature/subspace selection
Biclustering/association rules
Kernel methods
Probabilistic inference
Structured output prediction
Systems identification
Graph inference, completion, smoothing
Semi-supervised learning


Sequence annotation
Gene expression and post-transcriptional regulation
Inference of gene regulation networks
Gene prediction and whole genome association studies
Metabolic pathway modeling
Signaling networks
Systems biology approaches to biomarker identification
Rational drug design methods
Metabolic reconstruction
Protein function and structure prediction
Protein-protein interaction networks
Synthetic biology


Florence d’Alche Buc, Universite d’Evry-Val d’Essonne, Evry, France
Nir Friedman, The Hebrew University of Jerusalem, Jerusalem, Israel
Ursula Kummer, BIOQUANT, University of Heidelberg, Germany
Hans Lehrach, Max Planck Institute for Molecular Genetics, Berlin, Germany
Vebjorn Ljosa, The Broad Institute of MIT and Harvard, USA


Saöo Dûeroski, Jozef Stefan Institute, Ljubljana, Slovenia
Simon Rogers, University of Glasgow, UK
Guido Sanguinetti, University of Sheffield/University of Edinburgh, UK