Annonce en lien avec l’Action/le Réseau : aucun
Laboratoire/Entreprise : LIMOS, UMR 6158 CNRS
Durée : 36 mois
Contact : vincent.barra@isima.fr
Date limite de publication : 2019-09-01
Contexte :
Projet européen NeuroDeRisk, 19 partenaires de 13 pays.
Sujet :
There is still a lack of complete understanding of the complex pathways and mechanisms leading to central and peripheral drug-induced neurotoxicity. However, these adverse effects are to a considerable extent (up to 25%) responsible for failure in clinical trials, affecting volunteers or patients administered with experimental drugs, and also, to some extent, albeit less than 1%, for hospitalizations caused by hidden neurotoxic adverse effects of marketed drugs.
The ambition of NeuroDeRisk (Neurotoxicity De-Risking in Preclinical Drug Discovery) H2020 project is to bundle the scientific expertise of experimental and theoretical scientists and to collaborate with computer scientists to address the challenge of preclinical prediction of neurotoxicity using a fully integrated approach: by linking unique expertise for building in vitro and in vivo models with in silico prediction tools, the project will establish a new and validated toolbox for preclinical prediction of neurotoxicity in humans.
The PhD subject is centered on the development of new machine learning strategies to optimize the identification of predictive and alerting biomarkers of neurotoxicity. The objectives here will be:
– to classify subjects (with respect to neurotoxicity based on data (collected by previous extensive behavioral and biochemical signatures of neurotoxicity in vivo, in vitro and ex vivo);
– to compute low dimensional manifolds on which subjects lie, and be able to explore the trends and directions of all possible neurotoxic effects in the manifold;
– to be able to early predict biomarkers of neurotoxicity, based on only a subset of data or on new features derived from the original data.
Both classical and innovative machine learning algorithms will be explored.
Having selected the most promising biomarker candidates from in vivo, in vitro, and ex vivo studies, in silico prediction of biomarkers using a suitably modified version of a predictive platform will be performed. Additional bioinformatics tools will be used in biomarker validation and prioritization by other partners of the European project.
Profil du candidat :
Candidat ouvert aux aspects méthodologiques et aux applications dans les domaines biologiques.
Capacité de dialogue important avec des acteurs issus de domaines multiples.
Formation et compétences requises :
Compétences en analyse de données, apprentissage statistique.
Compétence en développement python et librairies associées (scikit learn, Tensorflow, Keras,…)
Adresse d’emploi :
LIMOS UMR 6158 CNRS
1 rue de la chébarde
63173 AUBIERE