Karine AUDOUZE

T3S-1124 - UMR-S 1124
Université Paris Descartes
45 rue des Saints Pères
75270 Paris Cedex 06

Fax : +33 (0) 1 42 86 38 68

Pr. Karine AUDOUZE

Associate Professor
Europe department, MetaTox

karine.audouze@-Code to remove to avoid SPAM-univ-paris-diderot.fr
+33 1 42 86 40 10

Student :

Qier WU

Karine Audouze is Associate Professor in Systems biology and Bioinformatics. She is responsible on the Systems Toxicology axis, under the MetaTox team. Her main research interest is in the development of computational systems biology and predictive toxicology models to investigate the effects of exposure to environmental chemicals on human health. Her teaching activities are complementary to her scientific skills (from chemoinformatics to bioinformatics). She is the principal investigator of the European H2020 project OBERON, and the ANR project CREATIvE.

 

Karine Audouze is a systems biologist with a background in computational chemistry. After a PhD in Chemistry/Bioinformatics at the University of Orléans, France (2001), she worked six years as a computational scientist within pharmaceutical industries (in the medicinal chemistry area) in Denmark, and seven years as Associate Professor in integrative systems biology at the Technical University of Denmark. In 2014, she became associate prof. at Paris Diderot University, and since 2017 she’s part on the 1124 unit.

Her expert knowledge is in the development of network biology and predictive toxicology models by integrating heterogeneous data types. She builds upon a solid background in statistical analyses, data mining, omics analysis and development & use of large-scale databases. She is now using these techniques to ascertain the potential links between environmental chemicals and adverse health outcomes and the potentials for health-environment interactions. She is PI or involved as scientist in several national and international projects (EU H2020, FP7, NIEHS, ANR), where her work is focused on providing computational models to support the integration and interaction between wet- and dry labs; and on developing new innovative tools (for example based on artificial intelligence).

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