Trung Ha | Julien Chiquet

Trung’s PhD was co-supervised with Marie-Laure Martin-Magniette (DR INRA/URGV) and Guillem Rigaill (MCF, Évry) (25%/50%/25%), from 2013 to 2016.

Summary

This thesis develops a penalized multivariate learning approach that jointly infers gene expression levels and the underlying gene regulatory network, rather than treating network inference as a separate step performed on already-estimated expression data. By coupling the two estimation problems within a single penalized framework, the approach aims to improve the accuracy of both the expression estimates and the inferred regulatory relationships, particularly when expression data are noisy or partially observed.

PhD manucript

2016

PhD thesis

A multivariate learning penalized method for a joined inference of gene expression levels and gene regulatory networks

Trung Ha

Université Paris-Saclay, 2016

Abs Website

This thesis develops a penalized multivariate learning approach that jointly infers gene expression levels and the underlying gene regulatory network, rather than treating network inference as a separate step performed on already-estimated expression data. By coupling the two estimation problems within a single penalized framework, the approach aims to improve the accuracy of both the expression estimates and the inferred regulatory relationships, particularly when expression data are noisy or partially observed.

Journal papers

Enhancing Differential analysis with network inference

T. Ha, J. Chiquet, M.-L. Martin-Magnette, and 1 more author

chapter in book for GGM inference

The full manuscript is also available directly from the university library.