Julie Aubert

The paper investigates microbial community composition and its relationship to biodegradation potential, finding that presence/absence data and machine learning models improve prediction of mineralisation.

The paper proposes a nonlinear two-step model integrating proxy intermediate omics traits to improve phenotypic prediction in soybean, showing better accuracy than linear models.

This paper introduces an efficient framework for ridge RDA to analyze high-dimensional omics data by addressing computational challenges through SVD and reduced memory storage.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

The paper develops a protocol to build multi-degrading bacterial communities for bioremediation of pesticide-contaminated soils using genomic prediction and coalescence.

This paper models the metabolism of a three-species bacterial community during cheese production using genome-scale models and omics data integration, accurately predicting community dynamics and interactions.