UR1404 Applied Mathematics and Informatics from Genome to the Environment (MaIAGE)



The MaIAGE laboratory gathers mathematicians, computer scientists, bioinformaticians and biologists to tackle problems from biology, agronomy and ecology. Our research concerns processes at various levels, ranging from molecular, cellular or multicellular levels to organisms, populations, and entire ecosystems.

MaIAGE develops new methods in mathematics, statistics, and computer science, which may be generic or driven by specific biological problems. A particular focus is on developing software, databases, ontologies and web services to be used by biologists in analyzing data or mining the scientific literature.

A main expertise of the laboratory is in statistical inference and dynamic modelling, along with bioinformatics, automatic control and algorithmics. Research and engineering activities are based on strong involvement in the target disciplines: ecology, environment, molecular biology and systems biology.

Latest publications

Aubert, J., Schbath, S. and Robin, S. (2021) Model-based biclustering for overdispersed count data with application in microbial ecology. Methods in Ecology and Evolution, , DOI

Ferré, A., Deléger, L., Bossy, R., Zweigenbaum, P., Nédellec, C. (2020) C-Norm: a neural approach to few-shot entity normalization. BMC Bioinformatics, 21, https://doi.org/10.1186/s12859-020-03886-8

Dérozier S, Nicolas P, Mäder U, Guérin C (2021) Genoscapist: online exploration of quantitative profiles along genomes via interactively customized graphical representations. BIOINFORMATICS, , doi: 10.1093/bioinformatics/btab079

Bystrova, D., Arbel, J., Kon Kam King, G., Deslandes, F. (2021) Approximating the clusters' prior distribution in Bayesian nonparametric models. Third Symposium on Advances in Approximate Bayesian Inference, Volume : , https://openreview.net/forum?id=J0SSW5XeWUY

Bray J.P., O'Reilly-Nugent, A., Kon Kam King, G., Sarit Kaserzon, Nichols, S. J., Mac Nally, R., Thompson, Ross M., Kefford, B. J. (2020) Can SPEcies At Risk of pesticides (SPEAR) indices detect effects of target stressors among multiple interacting stressors?. Science of The Total Environment, Volume : in press, https://doi.org/10.1016/j.scitotenv.2020.142997

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