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.
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.
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 are:
|Active learning/experimental design
||Genome-wide association studies
|Data integration/fusion/multi-view learning
||Metabolic modeling and reconstruction
||Protein function and structure prediction
||Protein-protein interaction networks
|Machine learning algorithms
||Rational drug design
|Multitask/structured output prediction
||MINES ParisTech and Institut Curie, Paris, France
||University of Manchester, UK
Scientific Program Committee 2017
|For further information, please contact the Chairs.