Matthieu Vignes

TL;DR: A simple yet very powerful meta-analysis is proposed, which combines a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse gene regulatory networks from different genetical genomics data sets and was ranked first among the teams participating in Challenge 3A.

TL;DR: A new approach to study the dynamics of root meristem distribution in soil, using the relationship between the increase in root length density and the root meristsem density to suggest the waves of meristems observed in root systems of barley seedlings exploring the soil might represent a more general and fundamental aspect of plant rooting strategies for securing soil resources.

TL;DR: A probabilistic model that has the advantage to account for individual data and pairwise data simultaneously simultaneously is proposed and points out the gain in using such an approach.

TL;DR: This paper presents a clustering algorithm dealing with incomplete data in a Hidden Markov Random Field context that tackles the missing value issue in a probabilistic framework and still allows to reconstruct missing observations a posteriori without imposing any pre-processing of the data.

TL;DR: In this paper, the authors examined the responses of 32,423 expressed sequences to drought and to abscisic acid and selected 145 co-expressed transcripts to identify patterns of molecular function and genetic diversity in GRNs.