MLSB17, the Eleventh International Workshop on Machine Learning in Systems Biology took place as a Special Session at ISMB/ECCB 2017 in Prague, Czech Republic, on July 25, 2017.

The aim of this special session 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.


Biology is rapidly turning into an information science, thanks to enormous advances in the ability to observe the molecular properties of cells, organs and individuals. This wealth of data allows us to model molecular systems at an unprecedented level of detail and to start to understand the underlying biological mechanisms. This field of systems biology creates a huge need for methods from machine learning, which find statistical dependencies and patterns in these large-scale datasets and that use them to establish models of complex molecular systems. MLSB is a scientific forum for the exchange between researchers from Systems Biology and Machine Learning, to promote the exchange of ideas, interactions and collaborations between these communities.


We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis. A non-exhaustive list of topics suitable for this workshop is:

Methods Applications
Active learning/experimental design Biomarker identification
Bayesian methods Epigenetics
Clustering/biclustering Genome-wide association studies
Data integration/fusion/multi-view learning Metabolic modeling and reconstruction
Deep learning Metabolomics
Feature/subspace selection Precision medicine
Graph inference/completion Protein function and structure prediction
Kernel methods Protein-protein interaction networks
Machine learning algorithms Rational drug design
Multitask/structured output prediction Regulatory genomics
Probabilistic inference Sequence annotation
Semi-supervised learning Signaling networks
Systems identification Synthetic biology
Time-series analysis Transcriptomics


MLSB17 Chairs

Chloé-Agathe Azencott MINES ParisTech and Institut Curie, Paris, France
Magnus Rattray University of Manchester, UK


Scientific Program Committee 2017

Michael Beer John Hopkins University
Andreas Beyer University of Cologne
Karsten Borgwardt ETH Zurich
Celine Brouard Aalto university
Chao Cheng Dartmouth Medical School
Gal Chechik Bar Ilan University
Jason Ernst UCLA
Pierre Geurts University of Liege
Markus Heinonen Aalto University
Antti Honkela University of Helsinki
Laurent Jacob Lyon I University
Lars Kaderali University Medicine Greifswald
Stefan Kramer Johannes Gutenberg University Mainz
Anshul Kundaje Stanford University
Hiroshi Mamitsuka Kyoto University / Aalto University
Yves Moreau KU Leuven
Sara Mostafavi UBC
Bernard Ng University of British Columbia
Mahesan Niranjan University of Southampton
Uwe Ohler Max Delbrueck Center & Humboldt University
Nico Pfeifer Department of Computer Science, University of Tübingen
Gerald Quon University of California, Davis
Min Martin Renqiang NEC Laboratories America
Simon Rogers Department of Computing Science, University of Glasgow
Guido Sanguinetti University of Edinburgh
Alexander Schliep Gothenburg University
Li Shen Icahn School of Medicine at Mount Sinai
Motoki Shiga Gifu University
Oliver Stegle EMBL-European Bioinformatics Institute
Giorgio Valentini Universita' degli Studi di Milano
Aki Vehtari Helsinki University of Technology
Jean-Philippe Vert Mines ParisTech
Jinbo Xu Toyota Technological Institute at Chicago



For further information, please contact the Chairs.