We invite submissions to MLMG 2022, the ECML-PKDD 2022 workshop on machine learning for microbial genomics to be held virtually, on September 23rd.
The
improvement and increasing accessibility of Next Generation Sequencing
data calls for innovative data analysis strategies to extract meaningful
and actionable information. Machine learning approaches are more and
more popular in this context, offering alternative and complementary
tools beyond bioinformatics to analyze genomic data. However, despite
the recent advances, their application remains challenging and new
approaches that are robust, scalable to large-scale high-dimensional
data and interpretable are needed.
This workshop aims to bring
together interdisciplinary researchers working at the interface of
machine learning and computational biology for the study of microbial
genomic data to discuss recent advances in this field. We will also have
the privilege of hosting two distinguished speakers from this community
to discuss and present their current research.
Typical topics of interest include:
- Improving microbial genome wide association studies (GWAS).
- Phenotype prediction from microbial genomes.
- Inferring population parameters from a set of microbial genomes.
- Visualization of microbial genomes in a way that highlights relevant
elements with respect to a phenotype of interest.
- Study of the constitution of a microbial flora.
Key dates:
- Submission deadline: June 20th 2022 (instructions here)
- Decisions: July 13th 2022
- Online workshop: September 23rd
Meriem El Azami
Laurent Jacob
Flora Jay
Pierre Mahé
ECML-PKDD 2022 workshop on machine learning for microbial genomics
Lea Boulos joined the lab in February as a master 2 student. She works with Burak Yelmen, Guillaume Charpiat and I on generative models for large-scale genomic data - based on our previous work creating realistic genomes based on generative neural networks (GANs and RBMs). Lea's stipend is supported by ANR.
Lindsay Goulet also joined (in May) as a master 1 student working with Fanny Pouyet, Louis Ollivier and myself on demographic inference for yeast populations. Lindsay's stipend is upported by Fanny's grant.
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