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GD Data, Algorithms and Computation

Coordinators: Nadine Hilgert and Pascal Poncelet

This General Direction (GD) promotes researches that aim to extract, represent, and manage knowledge associated with large volumes of data (heterogeneous and complex) from environment and life sciences. This GD relevant for the decision maker. It also deals with designing algorithms and computational methods to process large-scale data. One very recent and important contribution on this topic is the AgroPortal project[1] which aims at developing and supporting an international reference ontology repository for agronomy. This work is pursued with Stanford University, into the context of a Marie Sklodowska-curie EU fellowship. Another example of data classification is about the inferring transcriptional gene regulatory networks from transcriptomic datasets, which is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. This research work has recently published in a Nature journal[2].


[1]C. Jonquet, A. Toulet, E. Arnaud, S. Aubin, E. Dzalé Yeumo, V. Emonet, J. Graybeal, M.A. Laporte, M.A. Musen, V. Pesce, P. Larmande (2018), AgroPortal: A vocabulary and ontology repository for agronomy, Computers and Electronics in Agriculture, Volume 144, Pages 126-143.

[2]C. Carré, A. Mas, G. Krouk (2017) Reverse engineering highlights potential principles of large gene regulatory network design and learning, npj Systems Biology and Applications 3:17;