Yves Moreau

LGTM couples a neural topic model with additive basis-function Gaussian processes, learning microbial co-abundance topics and how covariates relate to their trajectories in longitudinal gut microbiome data.

DESPOT improves protein-ligand docking by introducing anisotropic scoring potentials that outperform isotropic methods in docking accuracy and virtual screening.

An automated pipeline turns the PDB into 178,263 repaired, protonated, restraint-minimized protein-ligand complexes; scoring functions trained on them beat HiQBind-trained ones on CASF-2016 ranking and docking power.

PRISM-G quantifies privacy risk in synthetic genome data by assessing genetic proximity, familial patterns, and trait-linked exposure, showing vulnerabilities vary across models and marker densities.

The paper identifies how batch correction methods can distort gene-gene correlations and introduces a metric to quantify this issue.

This paper identifies flaws in current Drug Response Prediction evaluation methods and proposes new strategies to improve their reliability and generalization.

geneRNIB provides a benchmark for gene regulatory network inference, showing simpler models often outperform complex ones in perturbation-informed metrics.

MetDecode uses methylation patterns to estimate tumor proportion and identify tissue origin in cell-free DNA samples from cancer patients.

This paper introduces an optimal transport-based domain adaptation method to correct technical biases in cfDNA sequencing data, improving cancer detection and enabling cohort integration.

WiNGS enables federated analysis of omics data across a consortium without centralized storage, supporting variant interpretation for rare disease diagnosis.

The paper analyzes genetic variation in ACE2 across Iranian populations to assess its association with COVID-19 severity, finding limited impact of exonic variants on disease outcomes.

This paper identifies ibrutinib as a VEGFR2 inhibitor through structure-based drug repositioning, revealing its polypharmacological action beyond BTK.

The paper evaluates drug repositioning approaches by analyzing 100 drugs, 200 targets, and 300 indications, finding that disease and target-centric methods dominate over drug-centric ones.

This paper introduces VarCoPP, a machine-learning method that identifies pathogenic variant combinations in gene pairs, achieving high accuracy and providing interpretable explanations for predictions.

The paper uses multi-task learning to link drug targets and pathway activation, revealing interactions that guide tissue-specific combination therapies.