© Oré |
Flora Jay - PhD - population genetics
statistic and machine learning methods applied to population genetics inference
First research day of LEGO (working group of GdR BIM)
Dear colleagues,
We are happy to invite you to the first day of the LEGO (machine LEarning for GenOmics) working group of the GDR BIM on May 31, 2023 in Paris.
LEGO is a working group affiliated with the GDR BIM of the CNRS and dedicated to machine learning for genomics. Like many scientific fields, genomics has benefited from the rise of machine learning over the past decade. The French research community in machine learning for genomics is active but dispersed throughout the territory within teams traditionally competent in other methodological areas of genomics, such as statistics, algorithms or bioinformatics. The objective of the LEGO working group is to facilitate exchanges within this community, to foster collaborations and to allow monitoring of the progress made by the participating teams.
We welcome abstracts of up to 300 words on recently published or ongoing research related to machine learning for genomics (for talks or posters).
Abstracts must be submitted via https://lego2023.sciencesconf.org/
Deadline: May 5, 2023.
Location: SCAI Building Esclangon - Sorbonne University, Paris, France
See you soon
Laurent Jacob, Flora Jay, Elodie Laine and Raphael Mourad
for the Program Committee.
Email: lego@services.cnrs.fr
Website: https://gt-lego.cnrs.fr/
To stay informed of LEGO working group activities, you can subscribe to the mailing list by sending an email to:
with the subject line:
subscribe lego yourFirstName yourLastName
ECML-PKDD 2022 workshop on machine learning for microbial genomics
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
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.
DigiCosme Thematic School 2021 Graph as models in life sciences: Machine learning and integrative approaches - online - October 25th - 29th
We are organizing an online thematic school
in bioinformatics and machine learning. You can register and spread the news.
Registration form:
https://framaforms.org/digicosme-thematic-school-2021-registration-1626095320
(free but mandatory, limited number of places for the tutorials)
Yann Ponty, DR CNRS, LIX
Ariane Migault, Chargée de communication du Labex DigiCosme
Burak Yelmen is joining the lab as a postdoctoral fellow to extend our previous work on creating realistic genomes based on generative neural networks (GANs and RBMs). Burak's salary is supported by ANR.
T Sanchez, J Cury, G Charpiat, F Jay (2020). Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation. Molecular Ecology Ressources DOI:10.1111/1755-0998.13224 Link
Pierre is also helping beta testing and improving our DNADNA software developed by the lab and in particular by Erik Madison Bray based on initial code by Théophile and Jean.