Tutor: Ulysse Herbach (research scientist at Inria in the BIGS project team, currently working on modeling and estimation of circulating tumor DNA (ctDNA) dynamics for detecting resistance to targeted therapies.)
Topic: Statistical inference of gene networks from dynamic graphs
The approach used in this research project is to model gene interactions using graph theory, with the structure of the graph being determined using Bayesian inference.
We will implement our model using Python in order to obtain concrete results and verify the accuracy of our approach.
By using a combination of graph theory and Bayesian inference, we aim to gain a better understanding of the complex relationships between genes and their interactions.
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[Imane El Meouche, 2016] Imane El Meouche, Yik Siu, M. J. D. (2016). Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Scientific reports, page 2.
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[Meilă and Jaakkola, 2006] Meilă, M. and Jaakkola, T. (2006). Tractable Bayesian learning of tree belief networks. Statistics and Computing, 16(1) :77–92.
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[Schwaller et al., 2019] Schwaller, L., Robin, S., and Stumpf, M. (2019). Closed-form bayesian inference of graphical model structures by averaging over trees. Journal de la société française de statistique, 160(2) :1–23.