What's new on BionomeeX
We provide customized & innovative analysis tools (Machine Learning, AI, statistical modeling…) to support your biological, medical and environmental R&D projects.
Our solutions may include at the same time crafted algorithms and/or adapted computational solutions in a single package following your needs.
BionomeeX projects (collaborations, “in house” developements) will be presented in the blog section of this website [here].
CEO – Co Founder
Senior computer and data scientist, with a PhD in applied mathematics and a deep experience in Machine Learning.
CSO – Co Founder
Biologist, research director at CNRS. Gabriel was trained at NYU to apply machine learning for the deciphering of Gene Regulatory Networks in plants.
Expert – Co Founder
AI Engineer / ML OPS
AI and Software Intern
Carré C, Mas A, Krouk G.
Reverse engineering highlights potential principles of large gene regulatory network design and learning.
NPJ Systems Biology Appl. 2017
Krouk G, Mirowski P, LeCun Y, Shasha DE, Coruzzi GM.
Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate.
Genome Biology. 2010
Carré C, Mas A.
Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach
Ann. Isup 2021
Ristova D, Carré C, Pervent M, Medici A, Kim GJ, Scalia D, Ruffel S, Birnbaum
KD, Lacombe B, Busch W, Coruzzi GM, Krouk G.
Combinatorial interaction network of transcriptomic and phenotypic responses to nitrogen and hormones in the Arabidopsis thaliana root.
Science Signaling. 2016
Krouk G, Carré C, Fizames C, Gojon A, Ruffel S, Lacombe B.
GeneCloud Reveals Semantic Enrichment in Lists of Gene Descriptions.
Molecular Plant (Cell Press). 2015
Hilgert N., Mas A., Verzelen N.
Minimax adaptive tests for the Functional Linear model.
Annals of Statistics. 2013.