MLSB 2017 - Machine Learning in Systems Biology (MLSB 2017)

The 11th International Workshop on Machine Learning in Systems Biology
http://www.mlsb.cc
Organized as a Special Session at ISMB/ECCB 2017
July 25, 2017
Prague, Czech Republic
*Abstract submission deadline: May 18, 2017*
***
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.
The aim of MLSB 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.
KEY DATES
Submission deadline: May 18, 2017
Author notification: June 8, 2017
Early bird registration deadline ISMB/ECCB 2017: June 15, 2017
MLSB 2017: July 25, 2017
SUBMISSIONS
We encourage submissions presenting novel methods for discovering
complex structures (e.g. interaction networks, molecular structures),
statistical machine learning methods for analysis of various
high-throughput -omics data, and methods supporting systems-level data
analysis.
We are calling for 1-4 page abstracts describing either:
- Original work on machine learning in systems biology. These
should describe method development and application, and we encourage
authors to submit recent ideas for discussion.
- Highlights describing computational aspects of work that has
recently been published or accepted for publication in a peer-reviewed
journal or at the main conference.
Abstracts will be considered for a talk or a poster presentation. The
abstract must be in PDF format (file extension .pdf) and can be up to 4
pages long, including all figures and references. If you only wish to be
considered for a poster, then a one page abstract is sufficient.
A non-exhaustive list of topics suitable for this workshop are:
- Active learning/experimental design
- Bayesian methods
- Biomarker identification
- Clustering/biclustering
- Data integration/fusion/multi-view learning
- Deep learning
- Epigenetics
- Feature/subspace selection
- Genome-wide association studies
- Graph inference/completion
- Kernel methods
- Machine learning algorithms
- Metabolic modeling and reconstruction
- Metabolomics
- Multitask/structured output prediction
- Precision medicine
- Probabilistic inference
- Protein function and structure prediction
- Protein-protein interaction networks
- Rational drug design
- Regulatory genomics
- Semi-supervised learning
- Sequence annotation
- Signaling networks
- Synthetic biology
- Systems identification
- Time-series analysis
- Transcriptomics
CHAIRS
Chloé-Agathe Azencott (MINES ParisTech, France)
Magnus Rattray (University of Manchester, UK)